Lodrik Adam, Sofia Benczédi, Stefan Favre, Delia Fuchs
Published
<<<<<<< Updated upstream
Invalid Date
=======
December 14, 2023
>>>>>>> Stashed changes
1 Introduction
1.1 Overview and Motivation
test
The global significance of the SDGs is our basis. The adoption of the SDGs by the United Nation in 2015 marked a significant global commitment to address pressing issues such as poverty, inequality, climate, change, and more. The fact that these goals were unanimously adopted by 193 member states underscores their importance. This prompted us to ask ourselves, can we evaluate the progress? What has really been done so far? Although the SDGs have attracted considerable attention and backing, it is essential to evaluate the events preceding and following their implementation. Understanding the actions taken and progress made is essential in determining if these global commitments are resulting in tangible enhancements to individuals’ lives. By examining the evolution of all countries and their respective contributions towards achieving the SDGs, we can develop a comprehensive understanding of collective efforts and identify potential disparities or gaps.
1.2 Related Work
Given the scale and the challenge that revolove around the SDGs, there are many researches that have been done on the topic. We have found some interesting ones that we found interesting to look at to get a better understanding of the topic. One of them, is none other than the report of the United Nations on the SDGs. This report is a comprehensive annual assessment of global and regional progress towards the SDGs. It is based on the latest available data and is prepared by the Statistics Division of the United Nations Department of Economic and Social Affairs. Find below a list of other interesting researches on the topic:
Focus on factors: What can explain the state of the countries regarding sustainable development? (we will analyse different factors: scores from the human freedom index, GDP per capita, military expenditures in % of GDP/government expenditure, unemployment rate, internet usage). See data description for more precise information about the factors.
Focus on time: How has the adoption of the SDGs in 2015 influenced the achievement of SDGs? (we want to compare the achievement (SDG scores: there are scores calculated even before the adoption) of the different countries before and after 2015 to see if the adoption of SDG gave a real “push” to sustainable development)
Focus on events: Is the evolution in sustainable development influenced by uncontrollable events, such as economic crisis, health crises and natural disasters? (we will analyse the impact of the COVID, natural disasters and conflicts (# deaths, damages, etc.) on the SDG scores). See data description for more precise information about how the impact of these events are materialized into data.
Focus on relationship between SDGs: How are the different SDGs linked? (We want to see if some SDGs are linked in the fact that a high score on one implies a high score on the other, and thus if we can make groups of SDGs that are comparable in that way).
2 Data
2.1 Sources
We are collecting our Data from the sustainability development report (SDG), the international labour organization (ILOSTAT), the World Bank, Our world in data, the CATO institute, one from Kaggle (disasters: we couldn’t find relevant accessible information from somewhere else) and GitHub. We found different datasets containing useful information in relation with the SDGs. The details about these data and the links are presented in the next section. Utilizing the kableExtra package, we provide a comprehensive list and corresponding links to our sources, as outlined below:
During the wrangling process, we added data to our table (D1_1_SDG) from different other datasets and match them based on the country code, and the year. The tables below show all the variables present in our 9 databases. We will then merge them to have our final table for the analysis.
2.2.1 Our databases
Sustainable Development Goals database (D1_1_SDG)
The Sustainable Development Goals (SDGs) are a universal set of 17 interlinked goals that were adopted by the United Nations in 2015 as part of the 2030 Agenda for Sustainable Development. These goals provide a shared blueprint for peace and prosperity for people and the planet, now and into the future.
This database give us comprehensive data on the unemployment rates for each country from 2000 to 2022. Originally, it included categories based on various age groups. However, for simplicity and coherence, the database has been streamlined to focus exclusively on the unemployment rates of individuals aged 15 years and older.
Variable Name
Explanation
code
Country code (ISO)
country
Country name
year
Year of the observation (2000-2022)
unemployment.rate
Unemployment rate (% of the population 15 years old and older)
GDP per capita database (D3_0_GDP_per_capita)
This database offers detailed information on the GDP per capita in dollars for various countries, covering the period from 2000 to 2022. It is designed to provide insights into the economic performance of each country over these years, measured through the lens of per capita GDP.
Variable Name
Explanation
code
Country code (ISO)
country
Country name
year
Year of the observation (2000-2022)
GDPpercapita
GDP per capita
Proportion of the GDP dedicated to Military expenditures database (D3_1_Military_expenditure_percent_GDP)
This database provides an insightful view of the proportion of their respective GDPs that countries have allocated to military expenditures. It covers the period from 2000 to 2022.
Variable Name
Explanation
code
Country code (ISO)
country
Country name
year
Year of the observation (2000-2022)
MilitaryExpenditurePercentGDP
Military expenditures in percentage of GDP
Internet usage database (D4_0_Internet_usage)
This database provides information on the percentage of the population that uses the internet in each country. It covers the period from 2000 to 2022.
Variable Name
Explanation
code
Country code (ISO)
country
Country name
year
Year of the observation (2000-2022)
internet.usage
Internet usage (% of the population)
Human freedom index database (D5_0_Human_freedom_index)
This database provides information on the Human Freedom Index (HFI) for each country. The HFI is a composite index that measures the degree to which people are free to enjoy important rights and freedoms. It covers the period from 2000 to 2022.
Variable Name
Explanation
code
Country code (ISO)
country
Country name
year
Year of the observation (2000-2022)
region
Part of the world, group of countries (e.g. Eastern Europe, Dub-Saharan Africa, South Asia, etc.)
pf_law
Rule of law, mean score of: Procedural justice, Civil, justice, Criminal justice, Rule of law (V-Dem)
pf_security
Security and safety, mean score of: Homicide, Disappearances conflicts, terrorism
pf_movement
Freedom of movement (V-Dem), Freedom of movement (CLD)
pf_religion
Freedom of religion, Religious organization, repression
pf_assembly
Civil society entry and exit, Freedom of assembly, Freedom to form/run political parties, Civil society repression
pf_expression
Direct attacks on the press, Media and expression (V-Dem), Media and expression (Freedom House), Media and expression (BTI), Media and expression (CLD)
Government consumption, Transfers and subsidies, Government investment, Top marginal tax rate, State ownership of assets
ef_legal
Judicial independence, Impartial courts, Protection of property rights, Military interference Integrity of the legal system Legal enforcementof contracts, Regulatory costs, Reliability of police
ef_money
Money growth, Standard deviation of inflation, Inflation: Most recent year, Freedom to own foreign currency
ef_trade
Tariffs, Regulatory trade barriers, Black-market exchange rates, Movement of capital and people
ef_regulation
Credit market regulations, Labor market regulations, Business regulations
Disaster list database (D6_0_Disaters)
This database provides information on the number of deaths, injured, affected and homeless people, as well as the total number of affected people and the total of infrastructure damages caused by disasters in each country. It covers the period from 2000 to 2021.
Variable Name
Explanation
code
Country code (ISO)
country
Country name
year
Year of the observation (2000-2021)
continent
Continents touched by the disasters such as floods, ouragan
total_deaths
Number of deaths caused by disasters
no_injured
Number of injured caused by disasters
no_affected
Number of affected caused by disasters
no_homeless
Number of homeless caused by disasters
total_affected
Total number of affected caused by disasters
total_damages
Total of infrastructure damages
COVID database (D7_0_COVID)
This database provides information on the number of people dead due to COVID, the number of COVID cases and the Government Response Stringency Index in each country. It covers the period from 2020 to 2022.
Variable Name
Explanation
code
Country code (ISO)
country
Country name
year
Year of the observation (2020-2022)
deaths_per_million
Number of people dead due to COVID
cases_per_million
Number of COVID cases
stringency
Government Response Stringency Index: composite measure based on 9 response indicators including school closures, workplace closures, and trave
Conflicts database (D8_0_Conflicts)
This database provides information on the number of deaths, the number of people affected and the maximum intensity of conflicts in each country. It covers the period from 2000 to 2022.
Variable Name
Explanation
code
Country code (ISO)
country
Country name
year
Year of the observation (2000-2022)
ongoing
Variable coded 1 for more than 25 deaths in intrastate conflict and 0 otherwise according to UCDP/PRIO Armed Conflict Dataset 17.1.
sum_deaths
Best estimate of deaths in all categories of violence (non-state, one-sided and state-based) recorded by the Uppsala Conflict Data Program in the country based on the UCDP GED dataset (unpublished 2016 data). The location of these events is used for estimating the extent of violence.
pop_affected
Share of population affected by violence in percentage (0 to 100) measured as described above based on population data from CIESIN, the PRIO-GRID structure as well as UCDP GED.
area_affected
Area affected by conflict
maxintensity
Two different intensity levels are coded: minor armed conflicts (1) and wars (2), Takes the max intensity of conflict in the country so that it is coded 2 if there is at least one war (>=1000 deaths in intrastate conflict) during the year. Data from UCDP/PRIO Armed Conflict Dataset 17.1.
2.3 Wrangling/cleaning
2.3.1 Pre-cleaning
To accommodate the large scale of the datasets, we pre-cleaned each one prior to merging. This streamlined the process, simplifying the cleaning of the final, combined dataset. The treatment of missing values wil be taken care of after merging our datasets.
2.3.1.1 Dataset on SDG
This is our main dataset, that we clean in order to keep the columns containing the following information: country name, country code, year, population, overall score and the SDGs scores.
We start by importing the data and converting it into a DataFrame. Next, we rename the columns and convert the scores into numeric variables.
#### D1_0_SDG missing values ####propmissing <-numeric(length(D1_0_SDG))for (i in1:length(D1_0_SDG)){ proportion <-mean(is.na(D1_0_SDG[[i]])) propmissing[i] <- proportion}variable_names <-colnames(D1_0_SDG)prop_missing_data <-data.frame(variable = variable_names, prop_missing = propmissing)ggplot(prop_missing_data,aes(x = variable,y = prop_missing)) +geom_bar(stat ="identity",fill ="skyblue",color ="black") +labs(title ="NAs by columns in the main dataset",x ="Variable",y ="Proportion of Missing Values") +theme(plot.title=element_text(size=10),axis.title.x=element_text(size=8),axis.title.y=element_text(size=8))+theme_minimal()+coord_flip()
Observing that the ‘population’ column contains numerous NAs, we investigate and discover that missing values are common, as some observations represent regions, not countries. Therefore, we can safely exclude these observations.
Code
#### D1_0_SDG missing values in population ####SDG0 <- D1_0_SDG %>%group_by(code) %>%select(population) %>%summarize(NaPop =mean(is.na(population))) %>%filter(NaPop !=0)ggplot(SDG0,aes(x = code,y = NaPop)) +geom_bar(stat ="identity",fill ="lightgreen",color ="black") +labs(title ="NAs by row in population variable are for regions and not countries",x ="Code",y ="Proportion of NAs") +theme_minimal() +theme(axis.text.x =element_text(angle =45,hjust =1))D1_0_SDG <- D1_0_SDG %>%filter(!str_detect(code, "^_"))
Now, there are no missing values in the ‘population’ variable, and we observe that it contains information on 166 countries.
We notice that NAs are present in only three SDG scores: 1, 10, and 14. Additionally, when a country has NAs, they occur across all years or not at all. Consequently, we decide to conduct further investigations on these three SDG scores to determine whether to include them in our analysis.
For goal 1, there are only 9.04% missing values in 15 different countries. Goal 1 being “End poverty”, we decide to keep it and only remove the countries with no information for the analysis.
For goal 10, there are only 10.2% missing values in 17 different countries. Goal 10 being “reduced inequalities”, we decide to keep it and only remove the countries with no information for the analysis.
For goal 14, there are 24.1% missing values in 40 different countries. Goal 14 being “life under water”, we decide not to keep it, because other SDG such as “life on earth” and “clean water” already treat similar subjects.
We will work with various datasets and merge them using the country code and year as key identifiers. To ensure accurate matching, we first verify that country names are encoded in UTF-8 format. Then, we standardize the names of the countries (requiring a custom match for Turkey) and the country codes, utilizing the countrycode library. Additionally, we compile a list of all country codes from the main database to filter the other datasets. Lastly, we complete the database to include all possible “country, year” combinations, ensuring the total number of rows remains unchanged.
For this first dataset, we reduced the size from 4,140 observations across 120 variables to 3,818 observations for 21 variables.
As said, this is now our main dataset. All subsequent datasets will be merged with this dataset. Therefore, for all the following datasets, we will make sure that we only keep data for the same countries and years as in this dataset. We have a total of 166 countries and the years range from 2000 to 2022.
2.3.1.2 Dataset on Unemployment rate
In this dataset, the initial step involves importing the data. Next, we ensure that the names and codes of the countries are formatted in UTF-8, preventing any discrepancies due to mismatches in country names. Following this, we modify the column names and filter the data to include only the relevant countries and years, specifically the years 2000 to 2022, covering 166 countries from our primary dataset.
For this first dataset, we reduced the size from 82,800 observations across 8 variables to 3,812 observations for 5 variables.
2.3.1.3 Dataset on GDP military Expenditures
We have three different databases which contain information on each countries over the years. Each year represent one variable. We want to extract three variables for our analysis: GDP per capita, military expenditures in percentage of the GDP and military expenditures in percentage of government expenditures.
Code
#### GDP per capita pre-cleaning ####GDPpercapita <-read.csv(here("scripts","data","GDPpercapita.csv"),sep =";")MilitaryExpenditurePercentGDP <-read.csv(here("scripts","data","MilitaryExpenditurePercentGDP.csv"),sep =";")MiliratyExpenditurePercentGovExp <-read.csv(here("scripts","data","MiliratyExpenditurePercentGovExp.csv"),sep =";")
After importing the data, we fill in the missing country codes using the column Indicator.Name, because we realized after some manipulations, that some of the country codes were false, but the next column contained the right ones.
Code
#### GDP per capita fill code ####fill_code <-function(data){ data <- data %>%mutate(Country.Code =ifelse(!grepl("^[A-Z]{3}$", Country.Code), Indicator.Name, Country.Code))}
We create a set of functions that we will apply to each database. First, remove the variables that we don’t need, which are the years before 2000. Second, make sure that the values are numeric and rename the year variables (because they all had an “X” before year number). Third, transform the database from wide to long, in order to match the main database. Fourth, transform the year variable into an integer variable and rearrange and rename the columns to match the ones of the other databases. Then, we apply these transformations to the three databases.
Code
#### Useful functions ####remove <-function(data){ years <-seq(1960, 1999) removeyears <-paste("X", years, sep ="") data <- data[, !(names(data) %in%c("Indicator.Name","Indicator.Code","X", removeyears))]}makenum <-function(data) {for (i in2000:2022) { year <-paste("X", i, sep ="") data[[year]] <-as.numeric(data[[year]]) }return(data)}renameyear <-function(data) {for (i in2000:2022) { varname <-paste("X", i, sep ="")names(data)[names(data) == varname] <-gsub("X", "", varname) }return(data)}wide2long <-function(data) { data <-pivot_longer(data, cols =-c("Country.Name","Country.Code"), names_to ="year", values_to ="data")return(data)}yearint <-function(data) { data$year <-as.integer(data$year)return(data)}nameorder <-function(data) {colnames(data) <-c("country","code","year","data") data <- data %>%select(c("code","country","year","data"))}cleanwide2long <-function(data){ data <-fill_code(data) data <-remove(data) data <-makenum(data) data <-renameyear(data) data <-wide2long(data) data <-yearint(data) data <-nameorder(data)}GDPpercapita <-cleanwide2long(GDPpercapita)MilitaryExpenditurePercentGDP <-cleanwide2long(MilitaryExpenditurePercentGDP)MiliratyExpenditurePercentGovExp <-cleanwide2long(MiliratyExpenditurePercentGovExp)
We rename the colums with the main information, standardize the country code and remove the countries that are not in our main database. We see that all the 166 countries are there.
There were only 157 countries that were both in the main SDG dataset and in these 3 datasets, but we suspected that some of the missing countries were in the database but not rightly matched. Indeed, Bahamas was in the database but instead of the code “BHS” there was “The”, for “COD” it was “Dem. Rep.”, for “COG” it was “Rep”, etc. We remarked that the code is in another column of the initial database: “Indicator.Name”. We went back to the initial database and before cleaning it we put the right codes (as seen above) and after rerunning the code we see that we have all our 166 countries from the initial dataset.
Code
#### Missing countries ####list_country_GDP <-c(unique(GDPpercapita$code))setdiff(list_country, list_country_GDP)#> character(0)
Code
#### Pre-cleaned datasets on GDP per capita ####D3_1_GDP_per_capita <- GDPpercapitaD3_2_Military_Expenditure_Percent_GDP <- MilitaryExpenditurePercentGDPD3_3_Miliraty_Expenditure_Percent_Gov_Exp <- MiliratyExpenditurePercentGovExp
Here are the first few lines of the cleaned dataset of GDP per capita:
To prepare the dataset on internet usage in the world to be merge with the other data, we first, import the data. Then, we keep only the year that we are interested in (2000 to 2022). We also rename the column and keep only the country that match the list of the countries in the main dataset on the SDG.
For this first dataset, we reduced the size from 6,570 observations across 4 variables to 3,433 observations for 4 variables.
2.3.1.5 Dataset on human freedom index
After importing the data from the CATO Institute website, we noticed that even if the file was called “Human Freedom Index 2022”, the available observations were only going from 2000 up to 2020. We have decided first to modify it in order to match our other datasets, by renaming/encoding/standardizing the columns containing the country names.
Code
#### Human Freedom Index pre-cleaning 1 ####data <-read.csv(here("scripts", "data", "human-freedom-index-2022.csv"))#data in tibble datatibble <-tibble(data)# Rename the column countries into country to match the other datbasesnames(datatibble)[names(datatibble) =="countries"] <-"country"# Make sure the encoding of the country names are UTF-8datatibble$country <-iconv(datatibble$country, to ="UTF-8", sub ="byte")# standardize country namesdatatibble <- datatibble %>%mutate(country =countrycode(country, "country.name", "country.name"))
Once done, we could verify which countries were or were not present between these observations and our main SDG dataset. We have decided to keep the ones that were matching between the two datasets.
Code
#### Human Freedom Index pre-cleaning 2 ##### Merge by country namedatatibble <- datatibble %>%left_join(D1_0_SDG_country_list, by ="country")datatibble <- datatibble %>%filter(code %in% list_country)(length(unique(datatibble$code)))#> [1] 159# See which ones are missinglist_country_free <-c(unique(datatibble$code))setdiff(list_country, list_country_free)#> [1] "AFG" "CUB" "MDV" "STP" "SSD" "TKM" "UZB"# Turkey was missing but present in the initial database (it was a problem# when standardizing the country names of D1_0SDG_country_list#that we corrected) and the other missing countries are:#"AFG" "CUB" "MDV" "STP" "SSD" "TKM" "UZB" D5_0_Human_freedom_index <- datatibble
Then, we noticed that there were a lot of columns that were not important for us, as we had 141 variables taken into account. So we have decided to keep the ones that refers to the countries informations (such as code, year, ..) and their human freedom scores per category (pf for personnal freedom, ef for economical freedom).
Code
#### Human Freedom Index pre-cleaning 3 ##### Erasing useless columns to keep only the general ones. D5_0_Human_freedom_index <-select(D5_0_Human_freedom_index, year, country, region, hf_score, pf_rol, pf_ss, pf_movement, pf_religion, pf_assembly, pf_expression, pf_identity, pf_score, ef_government, ef_legal, ef_money, ef_trade, ef_regulation, ef_score, code)D5_0_Human_freedom_index <- D5_0_Human_freedom_index %>%rename(pf_law =names(D5_0_Human_freedom_index)[5], # Renames the 5th column to "pf_law"pf_security =names(D5_0_Human_freedom_index)[6] # Renames the 6th column to "pf_security" )
Here are the first few lines of the partialy cleaned dataset on Human Freedom Index scores:
For this first dataset, we reduced the size from 3’465 observations across 141 variables to 3339 observations for 4 variables.
2.3.1.6 Dataset on Disasters
For this dataset concerning the Disasters we imported the data from Kaggle as we couldn’t find the original dataset that is private coming from the EOSDIS SYSTEM, an interactive interface for browsing full-resolution, global, daily satellite images from NASA. Once we made sure that our file called “Disasters” was convert into a data frame, we selected some specific columns that we where interested in.
Because we knew that our file showed all the disasters in each country over the years (between 1970-2021) and that we wanted to focus on a specific period, we filtered our data to show the years between 2000 and 2022. Then we rearranged our data, changing the data types of all the columns and their names in order to match our other datasets.
Code
#### Disasters pre-cleaning 2 ##### Rearrange the columns, changed the type of data, renamed the columnsRearanged_Disasters <- Disasters %>%filter(Year >=2000& Year <=2022) %>%mutate(code =as.character(ISO),country =as.character(Country),year =as.integer(Year),continent =as.character(Continent),disaster.subgroup =as.character(Disaster.Subgroup),disaster.type =as.character(Disaster.Type),location =as.character(Location),total.deaths =as.numeric(Total.Deaths),no.injured =as.numeric(No.Injured),no.affected =as.numeric(No.Affected),no.homeless =as.numeric(No.Homeless),total.affected =as.numeric(Total.Affected),total.damages =as.numeric(Total.Damages...000.US..) )
We then grouped the data by “year”, “code”, “country” and “continent” and summarize the data. Here you can see that we re-selected specific columns as we saw that our first pre-selection was still too wide and some variables as the disaster.subgroup and disaster.type weren’t pertinent.We arranged the columns based on “code,” “country,” “year,” and “continent” to match the other datasets.
Finally we filtered our disasters data to keep only the countries that are present in our main dataset. We analysed the missing countries and identified three countries (BHR, BRN, MLT) that are unexpectedly missing.
Code
#### Disasters pre-cleaning 4 ####D6_0_Disasters <- D6_0_Disasters %>%filter(code %in% list_country)length(unique(D6_0_Disasters$code))#> [1] 163# Here we see which countries are missinglist_country_disasters <-c(unique(D6_0_Disasters$code))setdiff(list_country, list_country_disasters)#> [1] "BHR" "BRN" "MLT"
Here are the first few lines of the cleaned dataset on Disasters:
This dataset contains information on the COVID19 pandemic between 2020 and 2022. The observation are by year, month, day. After importing the database, we transform the date in format YYYY-MM-DD in order to only keep the year.
We perform a first round of investigation of the missing values before aggregating the values by year. We begin with the variables “cases per million” and “deaths per million”: seeing that for each country, we have either only missing values, either a very low percentage of missing values (~1%), we can compute the sum over each year and ignore the missing values without altering the data. Indeed, where all the values are missing, the computation will return a NA. We then look at the “stringency” variable and we have 3 scenarios:
~20% of missing values: we ignore missing values when computing the mean to have an idea of stringency each year (because we compute the mean stringency over the year, if some days are missing, it is not a problem, it can not evoluate that fast).
all are missing: we can ignore the missing values when computing the mean, because it will still return a missing value
almost all are missing: here the mean doesn’t make sense -> we will replace the values by NAs to be coherent. The countries with this issues are: ERI, GUM, PRI and VIR. We verify if they are in our main dataset and since none of these countries are, we can ignore the issue, the lines will be remove later anyway.
We aggregate the observations of all days of a year in one observation per country using the mean.
Now that all the variables of interest are aggregated by year, we remove all the variables that we don’t need and rename all the remaining variables to match the main dataset.
We remove the years that exceed 2022, we make sure that the country codes are all iso codes with 3 letters (we observe that sometimes they are preceded by “OWID_”) and we standardize the country codes.
Code
#### COVID years and code cleaning ####COVID <- COVID[COVID$year <=2022, ]COVID$code <-gsub("OWID_", "", COVID$code)COVID$code <-countrycode(sourcevar = COVID$code,origin ="iso3c",destination ="iso3c")
We remove the observations of countries that aren’t in our main dataset on SDGs and find that all the 166 countries that we have in the main SDG dataset are also in this one.
For our conflicts dataset, we imported the data from “The World Banck” data catalog. Once we made sure that our file called “Disasters” was convert into a data frame, we selected some specific columns that we where interested in.
Our file showed all the Conflicts and consequences per country over the years (between 2000-2016). We couldn’t find a better and more complete dataset, As we consider conflicts as events, we will only take into account results between 2000 and 2016. Then we rearranged our data, changing the data types of all the columns and their names in order to match our other datasets. We grouped the data by ” year”, “country”, re-selected some variables and summarize the data.
Code
#### Conflicts rearranging 1 ####Rearanged_Conflicts <- Conflicts %>%filter(year >=2000& year <=2022)%>%mutate(ongoing =as.integer(ongoing),country =as.character(country),year =as.integer(year),gwsum_bestdeaths =as.numeric(gwsum_bestdeaths),pop_affected =as.numeric(pop_affected),area_affected =as.numeric(area_affected),maxintensity =as.numeric(maxintensity), )# Group the data by "year", "country" and summarize the dataConflicts <- Rearanged_Conflicts %>%group_by(year, country) %>%summarize(ongoing =sum (ongoing, na.rm =TRUE),sum_deaths =sum(gwsum_bestdeaths, na.rm =TRUE),pop_affected =sum(pop_affected, na.rm =TRUE),area_affected =sum(area_affected, na.rm =TRUE),maxintensity =sum(maxintensity, na.rm =TRUE), )
After we Selected specific columns from the summarized data and arrange the data by our specified columns. To make our dataset compatible with the main one and let the merging face succeed, we dd some adjustment concerning the country names’ to ensure the compatibility. Then we standardize and merge by country names to finally rearrange the data to retain only the countries present in our main dataset. Note that in the end we can see that only one country is missing that wasn’t in the initial conflicts database: BLR
Code
#### Conflicts rearranging 2 ####conflicts <- Conflicts %>%select(country, year, ongoing, sum_deaths, pop_affected, area_affected, maxintensity) %>%arrange(country, year)conflicts$country <-iconv(conflicts$country, to ="UTF-8", sub ="byte")conflicts <- conflicts %>%mutate(country =countrycode(country, "country.name", "country.name"))conflicts <- conflicts %>%left_join(D1_0_SDG_country_list, by ="country")conflicts <- conflicts %>%select(code, country, year, ongoing, sum_deaths, pop_affected, area_affected, maxintensity) %>%arrange(code, country, year)D8_0_Conflicts <- conflicts %>%filter(code %in% list_country)(length(unique(conflicts$code)))#> [1] 166# See which countries are missinglist_country_conflicts <-c(unique(conflicts$code))setdiff(list_country, list_country_conflicts)#> [1] "BLR"
Here are the first few lines of the cleaned dataset on Conflicts:
2.3.2.1 Filing missing continents and regions colomns
When we merged our dataset, we noticed that some countries were not assigned their corresponding continents and/or region. This issue arose because we sourced the continent and region data from secondary databases, not from our main one. We now add this the corresponding missing continents and regions.
Code
#### Filling missing continents and regions ##### Update all_Merge with region and continent informationall_Merge <- all_Merge %>%group_by(country) %>%mutate(continent =ifelse(is.na(continent), first(na.omit(continent)), continent),region =ifelse(is.na(region), first(na.omit(region)), region) ) %>%ungroup() %>%mutate(continent =case_when( code %in%c("BHR") ~"Asia", code %in%c("BRN") ~"Asia", code %in%c("MLT") ~"Europe",TRUE~ continent ), region =case_when( code %in%c("AFG", "MDV") ~"South Asia", code %in%c("CUB") ~"Latin America & the Caribbean", code %in%c("STP", "SSD") ~"Sub-Saharan Africa", code %in%c("TKM", "UZB") ~"Caucasus & Central Asia",TRUE~ region))
We order the database, beginning by the information on the country, the year, the continent and the region.
Code
#### Ordering the database and saving it as .CSV ####all_Merge <-as.data.frame(all_Merge) %>%select(code, year, country, continent, region, everything())write.csv(all_Merge, file =here("scripts","data","all_Merge.csv"))
Here are the first few lines of the final dataset:
Final structure of our merged database: each country of the 166 countries from D1_1_SDG are observed each year from 2000 to 2022, thus each row has a key composed of (code, year) that uniquely identifies an observation. The other columns are the variables listed above. Due to some countries having a lot of missing information we will have to eliminate some of them, but we will still have more than 2000 rows in our database.
2.3.3 Treatment of missing values
We load our final database and we visualize the missing values.We see that some variables have many NAs and that some patterns regarding row missingness emerge.
Code
#### Loading the final database to be cleaned ####all_Merge <-read.csv(here("scripts","data","all_Merge.csv"))# Remove unnecessary columnall_Merge <- all_Merge %>%select(-c(X))# Create a dataframe with the goals without NAs summarize in one column to#simplify the visualizationgoal_vars <- all_Merge %>%select(starts_with("goal")) %>%filter_all(all_vars(!is.na(.))) %>%colnames()to_plot_missing <- all_Merge %>%mutate(Goals_without_NAs =rowSums(!is.na(select(., all_of(goal_vars))))) %>%select(-c(goal2, goal3, goal4, goal5, goal6, goal7, goal8, goal9, goal11, goal12, goal13, goal15, goal16, goal17))vis_dat(to_plot_missing, warn_large_data =FALSE) +scale_fill_brewer(palette ="Paired") +theme(axis.text.x =element_text(angle =90, size =6),legend.text =element_text(size =8), # Adjust the size of legend textlegend.title =element_text(size =10) )
For each of our research question, we will start with the merged data set and deal with the missing value separately, because there are often NAs for the same row across many columns that will be used for the same question. This will allow us to not delete observations when we do not need to.
For question 1, we only keep the years until 2020, because most of the explanatory variables that we want to use (those coming from the human freedom index) only have values until 2020.
For question 3, we create 3 distinct databases according to the different type of event that we will analyse: disasters, COVID19 and conflicts. For the disasters, we only keep the years until 2021, because after this date, we don’t have data, moreover we decided to delete the country Bahrain, Brunei and Malta as we do not have any data concerning them. For the conflicts, we only keep the years until 2016, because after this date, we don’t have data. Concerning the conflict dataset, we decided to erase Belarus because once again we do not have any data concerning this country.
We begin by visualizing the missing values. To have a less messy graph we group all the goals wihtout NAs into one single variable. We decide to remove MilitaryExpenditurePercentGovExp, because it has too many missing values and it contains similar information to MilitaryExpenditurePercentGDP.We also remove hf_score, pf_score and ef_score, because there are many missing values and since these variables summarize the other ones, deleting them will not make us loose information.
Code
#### Visualizing missing values by variables ##### Create a dataframe with the goals without NAs summarize in one column to simplify the visualizationvariable_names <-names(data_question1)missing_percentages <-sapply(data_question1, function(col) mean(is.na(col)) *100)missing_data_summary <-data.frame(Variable = variable_names,Missing_Percentage = missing_percentages)missing_data_summary <- missing_data_summary %>%mutate(VariableGroup =ifelse(startsWith(Variable, "goal") & Missing_Percentage ==0, "Goals without NAs", as.character(Variable)))ggplot(data = missing_data_summary, aes(x =reorder(VariableGroup, Missing_Percentage), y = Missing_Percentage, fill = Missing_Percentage)) +geom_bar(stat ="identity") +geom_text(aes(label =ifelse(Missing_Percentage >1, sprintf("%.1f%%", Missing_Percentage), ""),y = Missing_Percentage),position =position_stack(vjust =1), # Adjust vertical positioncolor ="white", # Text colorsize =2, # Text sizehjust =1.05) +labs(title ="Percentage of Missing Values by Variable",x ="Variable",y ="Missing Percentage") +theme_minimal() +theme(axis.text.y =element_text(hjust =1, size=6 ),legend.text =element_text(size =8),legend.title =element_text(size =10)) +labs(fill ="% NAs") +coord_flip()data_question1 <- data_question1 %>%select(-c(MiliratyExpenditurePercentGovExp, hf_score, pf_score, ef_score))
Dealing with missing values in rows
We create a column with the number of missing values by country over all the variables, except goal 1 and goal 10 that we already discussed. We decide to remove the countries that have more than 50 missing values.
Here is the graph that allows us to visualize the countries that have missing values and how many , when there are more than 50 NAs in total.
Code
#### Number of missing values per country (>50 NAs) ####ggplot(see_missing1_1, aes(x = num_missing , y =reorder(code, num_missing), fill = num_missing)) +geom_bar(stat ="identity") +scale_fill_gradient(low ="lightgreen", high ="darkgreen") +theme_minimal() +theme(axis.text.y =element_text(hjust =1, size=8 ),legend.text =element_text(size =8),legend.title =element_text(size =10),plot.title =element_text(size=12)) +labs(title ="Number of missing values per country containing at least 50 NAs", x ="Number of Missing Values", y ="Countries")
We also look at patterns of missing values in the rows and see that except for the two goals with NAs that we discussed earlier and for the triplet “ef_money”, “ef_trade” and “ef_regulation” there are not well defined patterns. We removes the countries that have NAs in the three variables mentioned at the same time.
Code
#### Visualizing the missing values in the rows ####naniar::gg_miss_upset(data_question1, nsets=10, nintersects=11)data_question1 <- data_question1[rowSums(is.na(data_question1[, c("ef_money","ef_trade","ef_regulation")])) <3, ]data_question1 <- data_question1 %>%group_by(code) %>%filter(all(2000:2020%in% year)) %>%ungroup()
GDP per capita
Only Venezuela has missing values that we can not fill (because the evolution over time is not linear), so we delete the country.
For MilitaryExpenditurePercentGDP, We plot the evolution of MilitaryExpenditurePercentGDP along the years for each country containing missing values and distinguish the percentage of missing values with colors.
Code
#### Evolution of MilitaryExpenditurePercentGDP over the time ####MilitaryExpenditurePercentGDP1 <- data_question1 %>%group_by(code) %>%summarize(NaMil1 =round(mean(is.na(MilitaryExpenditurePercentGDP)),3)) %>%filter(NaMil1 !=0)filtered_data_Mil1 <- MilitaryExpenditurePercentGDP %>%filter(code %in% MilitaryExpenditurePercentGDP1$code) # countries with NAsfiltered_data_Mil1 <- filtered_data_Mil1 %>%group_by(code) %>%mutate(PercentageMissing =mean(is.na(MilitaryExpenditurePercentGDP))) %>%# Column % NAsungroup()Evol_Missing_Mil1 <-ggplot(data = filtered_data_Mil1) +geom_line(aes(x = year,y = MilitaryExpenditurePercentGDP,color =cut(PercentageMissing,breaks =c(0,0.1,0.2,0.3,1),labels =c("0-10%","10-20%","20-30%","30-100%")))) +labs(title ="Military expenditure in % of GDP over time",x ="Year",y ="Military expenditure in % of GDP") +scale_color_manual(values =c("0-10%"="blue","10-20%"="green","20-30%"="red","30-100%"="black"),labels =c("0-10%","10-20%","20-30%","30-100%")) +guides(color =guide_legend(title ="% NAs")) +facet_wrap(~ code, nrow =5) +theme(strip.text =element_text(size =6),axis.text.x =element_text(angle =45, size=6)) +scale_y_continuous(breaks =NULL)print(Evol_Missing_Mil1)
We delete the countries with more than 30% of missing values and for the countries with less than 30% of missing values and a linear evolution in time, we fill the missing values using linear interpolation.
Code
#### Deletion of countries with (>30% NAs) ####data_question1 <- data_question1 %>%filter(code!="ARE"& code!="BHS"& code!="BRB"& code!="CRI"& code!="HTI"& code!="ISL"& code!="PAN"& code!="SYR"& code!="VNM") list_code <-c("BDI", "BEN", "CAF", "CIV", "COD","GAB", "NER", "TGO", "TTO", "ZMB")for (i in list_code) { country_data <- data_question1 %>%filter(code == i) interpolated_data <-na.interp(country_data$MilitaryExpenditurePercentGDP) data_question1[data_question1$code == i, "MilitaryExpenditurePercentGDP"] <- interpolated_data}
Then, we look at the distribution of the variable per region. Seeing that all are skewed distributions, we decide to replace the remaining missing values, where there are less than 30% missing using the median by region.
Code
#### Distribution of the variable per region ####question1_missing_Military <- data_question1 %>%group_by(code) %>%mutate(PercentageMissing =mean(is.na(MilitaryExpenditurePercentGDP))) %>%# Column % NAsungroup() %>%group_by(region) %>%filter(sum(PercentageMissing, na.rm =TRUE) >0)Freq_Missing_Military <-ggplot(data = question1_missing_Military) +geom_histogram(aes(x = MilitaryExpenditurePercentGDP, fill =cut(PercentageMissing,breaks =c(0,0.1,0.2,0.3,1),labels =c("0-10%","10-20%","20-30%","30-100%"))),bins =30) +labs(title ="Distribution of Military expenditures in % of GDP",x ="Military expenditures in % of GDP",y ="Frequency") +scale_fill_manual(values =c("0-10%"="blue","10-20%"="green","20-30%"="red","30-100%"="black"),labels =c("0-10%","10-20%","20-30%","30-100%")) +guides(fill =guide_legend(title ="% NAs")) +facet_wrap(~ region, nrow =1)print(Freq_Missing_Military)data_question1 <- data_question1 %>%group_by(code) %>%mutate(PercentageMissingByCode =mean(is.na(MilitaryExpenditurePercentGDP)) ) %>%ungroup() %>%group_by(region) %>%mutate(MedianByRegion =median(MilitaryExpenditurePercentGDP, na.rm =TRUE),MilitaryExpenditurePercentGDP =ifelse( PercentageMissingByCode <0.3&!is.na(MilitaryExpenditurePercentGDP), MilitaryExpenditurePercentGDP,ifelse(PercentageMissingByCode <0.3, MedianByRegion, MilitaryExpenditurePercentGDP) ) ) %>%select(-PercentageMissingByCode, -MedianByRegion)
There are never more than 30% of NAs. We look at the evolution of the variable over time. We fill the missing values with linear interpolation, because all are increasing in time and they are almost straight lines, except for CIV that we delete.
Code
#### Evolution of the variable over time ####question1_missing_Internet <- data_question1 %>%group_by(code) %>%mutate(PercentageMissing =mean(is.na(internet_usage))) %>%# Column % NAsfilter(code %in% question1_missing_Internet$code)Evol_Missing_Internet <-ggplot(data = question1_missing_Internet) +geom_line(aes(x = year,y = internet_usage,color =cut(PercentageMissing,breaks =c(0,0.1,0.2,0.3,1),labels =c("0-10%","10-20%","20-30%","30-100%")))) +labs(title ="Evolution of internet usage over time",x ="Year",y ="Internet usage in %") +scale_color_manual(values =c("0-10%"="blue","10-20%"="green","20-30%"="red","30-100%"="black"),labels =c("0-10%","10-20%","20-30%","30-100%")) +guides(color =guide_legend(title ="% NAs")) +theme(axis.text.x =element_text(angle =45, size=6),axis.text.y =element_text(size=6))+facet_wrap(~ code, nrow =4)print(Evol_Missing_Internet)list_code <-setdiff(unique(question1_missing_Internet$code), "CIV")for (i in list_code) { country_data <- data_question1 %>%filter(code == i) interpolated_data <-na.interp(country_data$internet_usage) data_question1[data_question1$code == i, "internet_usage"] <- interpolated_data}data_question1 <- data_question1 %>%filter(code!="CIV")
Human freedom index
Personal freedom: law
The variable pf_law has (many) NAs, but only for one country: BLZ, so we decide to remove it.
Code
#### pf_law has NAs only for BLZ ####data_question1 <- data_question1 %>%filter(code!="BLZ")
Economic freedom: government
There are no more missing values, thanks to the previous steps.
Economic freedom: money
5 countries have missing values, but the percentage of missing values is always below 25%.
Code
#### Missing values in 5 countries (<25%) ####question1_missing_ef_money <- data_question1 %>%group_by(code) %>%summarize(Na_ef_money =mean(is.na(ef_money))) %>%filter(Na_ef_money !=0)
We look at the evolution of the variable over time, and for the countries with a linear evolution in time, we fill the missing values using linear interpolation.
Code
#### Evolution of economic freedom: money over time ####question1_missing_ef_money <- data_question1 %>%group_by(code) %>%mutate(PercentageMissing =mean(is.na(ef_money))) %>%# Column % NAsfilter(code %in% question1_missing_ef_money$code)Evol_Missing_ef_money <-ggplot(data = question1_missing_ef_money) +geom_line(aes(x = year,y = ef_money, color =cut(PercentageMissing,breaks =c(0,0.1,0.2,0.3,1),labels =c("0-10%","10-20%","20-30%","30-100%")))) +labs(title ="Evolution of economic freedom: money over time",x ="Year",y ="ef_money") +scale_color_manual(values =c("0-10%"="blue","10-20%"="green","20-30%"="red","30-100%"="black"),labels =c("0-10%","10-20%","20-30%","50-100%")) +guides(color =guide_legend(title ="% NAs")) +theme(axis.text.x =element_text(angle =45, size=6))+facet_wrap(~ code, nrow =1) +scale_y_continuous(limits =c(0, 10))print(Evol_Missing_ef_money)list_code <-c("GEO","MKD")for (i in list_code) { country_data <- data_question1 %>%filter(code == i) interpolated_data <-na.interp(country_data$ef_money) data_question1[data_question1$code == i, "ef_money"] <- interpolated_data}
Then, we look at the distribution of the variable per region. Seeing that all are skewed distributions, we decide to replace the missing values using the median by region.
6 countries have missing values, but the percentage of missing values is always below 25%.
Code
#### Missing values in 6 countries (<25%) ####question1_missing_ef_trade <- data_question1 %>%group_by(code) %>%summarize(Na_ef_trade =mean(is.na(ef_trade))) %>%# Column % NAsfilter(Na_ef_trade !=0)
We look at the evolution of the variable over time. For the countries where this evolution is linear, we fill in the missing values using linear interpolation.
Code
#### Evolution of economic freedom: trade over time ####question1_missing_ef_trade <- data_question1 %>%group_by(code) %>%mutate(PercentageMissing =mean(is.na(ef_trade))) %>%# Column % NAsfilter(code %in% question1_missing_ef_trade$code)Evol_Missing_ef_trade <-ggplot(data = question1_missing_ef_trade) +geom_line(aes(x = year,y = ef_trade, color =cut(PercentageMissing,breaks =c(0,0.1,0.2,0.3,1),labels =c("0-10%","10-20%","20-30%","30-100%")))) +labs(title ="Evolution of economic freedom: trade over time",x ="Year",y ="ef_trade") +scale_color_manual(values =c("0-10%"="blue","10-20%"="green","20-30%"="red","30-100%"="black"),labels =c("0-10%","10-20%","20-30%","50-100%")) +guides(color =guide_legend(title ="% NAs")) +theme(axis.text.x =element_text(angle =45, size=6))+facet_wrap(~ code, nrow =2) +scale_y_continuous(limits =c(0, 10))print(Evol_Missing_ef_trade)# Linear interpolation for "AZE", "BFA", "ETH", "GEO", "VNH"list_code <-c("AZE","GEO","MKD","MNG")for (i in list_code) { country_data <- data_question1 %>%filter(code == i) interpolated_data <-na.interp(country_data$ef_trade) data_question1[data_question1$code == i, "ef_trade"] <- interpolated_data}
Then, we look at the distribution of the variable per region. Seeing that the only region that still has missing values is a centered distribution, we decide to replace the missing values using the mean of the region.
There are no more missing values, thanks to the previous steps.
SDGs 1 and 10
We noticed earlier that there were only missing values for goals 1 and 10. As we did before, we have started to investigate where are located the NAs in our dataset for first goal1, then goal 10.
We had found that the missing values were located in only 5 countries. So we have decided to get rid of them. At this stage, there were only 42 remaining missing values. Then, we redo the same steps for goal 10.
We have found the 2 lasts countries containing missing values. Now, our dataset is completely clean and ready to be used for our question 1.
Data for question 2 and 4
We create a column with the number of missing values by country over all the variables, except goal 1 and goal 10 that we already discussed. Since there are no other missing values, we stop here.
Code
#### Missing values columns ####see_missing24 <- data_question24 %>%group_by(code) %>%summarise(across(everything(), ~sum(is.na(.))) %>%mutate(num_missing =rowSums(across(everything()))) %>%filter(num_missing >0))#> `summarise()` has grouped output by 'code'. You can override using#> the `.groups` argument.data_question24 <- data_question24 %>%group_by(country) %>%filter(!all(is.na(goal1)) &!all(is.na(goal10)))
Data for question 3
We create a column with the number of missing values by country over all the variables, except goal 1 and goal 10 that we already discussed. Since there are no other missing values, we stop here.
Disasters
We begin by visualizing the missing values.
Code
#### Visualizing missing values ####variable_names <-names(data_question3_1)missing_percentages <-sapply(data_question3_1, function(col) mean(is.na(col)) *100)missing_data_summary <-data.frame(Variable = variable_names,Missing_Percentage = missing_percentages)missing_data_summary <- missing_data_summary %>%mutate(VariableGroup =ifelse(startsWith(Variable, "goal") & Missing_Percentage ==0, "Goals without NAs", as.character(Variable)))ggplot(data = missing_data_summary, aes(x =reorder(VariableGroup, Missing_Percentage), y = Missing_Percentage, fill = Missing_Percentage)) +geom_bar(stat ="identity") +geom_text(aes(label =ifelse(Missing_Percentage >1, sprintf("%.1f%%", Missing_Percentage), ""), y = Missing_Percentage),position =position_stack(vjust =1), # Adjust vertical positioncolor ="white", # Text colorsize =3, # Text sizehjust =1.05) +labs(title ="Percentage of Missing Values by Variable",x ="Variable",y ="Missing Percentage") +theme_minimal() +theme(axis.text.y =element_text(hjust =1)) +coord_flip()
In this particular case, even if there are many missing values in our disaster dataset, we made the hypothesis that disaster events can not happen every year for every country given that these are uncontrollable and non-recurring events. Therefore the NAs that we encounter will become zeroes, implying that there have been no climatic disasters.
Code
#### Replacing NAs by 0 ####data_question3_1[is.na(data_question3_1)] <-0
COVID19
We look at the missing values for the three variables that are specific to COVID during the years of COVID: 2020 to 2022. We delete the countries that have NAs (only stringency has 6 countries with 100% NAs).
We replace the NAs of the other COVID columns (years 2000 t0 2019) by 0 (because we don’t have real missing, only introduced by merging with the other databases).
Code
#### Replacing NAs by 0 ####data_question3_2 <- data_question3_2 %>%mutate(cases_per_million =ifelse(is.na(cases_per_million), 0, cases_per_million),deaths_per_million =ifelse(is.na(deaths_per_million), 0, deaths_per_million),stringency =ifelse(is.na(stringency), 0, stringency) )
Conflicts
We create a column with the number of missing values by country over all the variables, except goal 1 and goal 10 that we already discussed.Two countries have missing values, we remove them (MNE and SRB).
Code
#### Removing countries because of missing values ####see_missing3_3 <- data_question3_3 %>%group_by(code) %>%summarise(across(-c(goal1, goal10), # Exclude columns "goal1" and "goal10"~sum(is.na(.))) %>%mutate(num_missing =rowSums(across(everything()))) %>%filter(num_missing >0))data_question3_3 <- data_question3_3 %>%filter(!code %in%c("MNE","SRB","SSD"))
3.1 Focus on the influence of the factors over the SDG scores
3.1.1 EDA: general exploratory data analysis on factors influencing the sdg goals
For this first part of our EDA, let’s try to check first the distribution of the variables selected for answering our 1st question.
Code
# Reshape the data from wide to long format for our sdg goals and our human freedom index scoreslong_df_goal_distribution <-pivot_longer(Correlation_overall, cols =starts_with("goal"), names_to ="Goal", values_to ="Value")long_df_goal_distribution$Goal <-with(long_df_goal_distribution, reorder(Goal, Value, FUN = mean))long_df_hfi_distribution <-pivot_longer(Correlation_overall, cols = pf_law:ef_regulation, names_to ="Category", values_to ="Value")long_df_hfi_distribution$Goal <-with(long_df_hfi_distribution, reorder(Category, Value, FUN = mean))ggplot(long_df_goal_distribution, aes(x = Value, y = Goal, fill =stat(x))) +geom_density_ridges_gradient(scale =3, size =0.3, rel_min_height =0.01) +scale_fill_viridis_c(name ="", option ="C") +theme(plot.title =element_text(hjust =0.5), # Center the titleplot.title.position ="plot") +labs(x ='Scores',y ='Goals',title ='SDG Goals Distribution')
As we can see, most of our goals have a left-skewed distribution, which shows that for most of the country concerned implemented good strategies for targeting the goals objectives. Some distribution have a longer distribution than other, which could be a proof of inequality in the investments made for implementing solutions. In another hand, we notice that the only right-skewed distribution is concerning the observations of goal 9, which is promoting infrastructures, innovation and inclusive and sustainable industrialization. Again, that could show means inequalities. Wealthier countries are able to invest more on these sustainable development goals.
Code
ggplot(long_df_hfi_distribution, aes(x = Value, y = Category, fill =stat(x))) +geom_density_ridges_gradient(scale =3, size =0.3, rel_min_height =0.01) +scale_fill_viridis_c(name ="", option ="C") +theme(plot.title =element_text(hjust =0.5), # Center the titleplot.title.position ="plot") +labs(x ='Scores',title ='Human Freedom Index Scores Distribution')
The distribution of the Human Freedom Index Score follows the same trend. Most of the scores are left-skewed, which means that countries tend to have in general good scores. The only scores not folowing are pf_law and ef_legal, which tend to have lower scores in general. Legal system, for civilians and countries, is changing slowly because it has a lot of implications over the situation within a country/between countries and because of the divergence of opinions. Therefore, investing more money for raising these scores will take more time than raising the scores of other goals.
Now let’s consider the remaining variables of the dataset dedicated to answering the influence of factors over our SDG goals scores. All these variables have right-skewed distribution. Taking the mode into account, most of the concerned countries in our data have an unemployment rate between 2 to 7%, a distribution of GDP per capita between $3’000-$10’000, a distribution of military expenditure in percentage of the GDP 10% to 20% and finally a internet usage between 0 and 10%.
These variables shows us even more the inequalities between the countries in our dataset. While most of our countries have low internet usage or/and a low GDP per capita, few countries are more developed, then mostly wealthier, and thus having better chances to get higher scores.
Code
#now, same for the remaining variables. No need to reshape our data as only one variable.unempl_d <-ggplot(Correlation_overall, aes(x = unemployment.rate, y =1, fill =stat(x))) +geom_density_ridges_gradient(scale =3, size =0.3, rel_min_height =0.01) +scale_fill_viridis_c(name ="", option ="C") +theme(plot.title =element_text(hjust =0.5, size =10), # Center the titleplot.title.position ="plot") +labs(y ='Density',title ='Distribution of Unemployment Rate')gdp_d <-ggplot(Correlation_overall, aes(x = GDPpercapita, y =1, fill =stat(x))) +geom_density_ridges_gradient(scale =3, size =0.3, rel_min_height =0.01) +scale_fill_viridis_c(name ="", option ="C") +theme(plot.title =element_text(hjust =0.5, size =10), # Center the titleplot.title.position ="plot") +labs(y ='Density', title ='Distribution of GDP per Capita')milit_d <-ggplot(Correlation_overall, aes(x = MilitaryExpenditurePercentGDP, y =1, fill =stat(x))) +geom_density_ridges_gradient(scale =3, size =0.3, rel_min_height =0.01) +scale_fill_viridis_c(name ="", option ="C") +theme(plot.title =element_text(hjust =0.5, size =10), # Center the titleplot.title.position ="plot") +labs(y ='Density',title ='Distribution of Military Expenditure (% of GDP)')internet_d <-ggplot(Correlation_overall, aes(x = internet_usage, y =1, fill =stat(x))) +geom_density_ridges_gradient(scale =3, size =0.3, rel_min_height =0.01) +scale_fill_viridis_c(name ="", option ="C") +theme(plot.title =element_text(hjust =0.5, size =10),plot.title.position ="plot") +labs(y ='Density',title ='Distribution of Internet Usage')grid.arrange(unempl_d,gdp_d,milit_d,internet_d, ncol =2, nrow =2)# Define UI for applicationui <-dashboardPage(dashboardHeader(title ="Interactive Plots"),dashboardSidebar(actionButton("switchPlot", "Switch to Next Plot") ),dashboardBody(plotOutput("distPlot") ))# Define server logicserver <-function(input, output) {# Reactive value to store which plot is currently active currentPlot <-reactiveVal("goal")observeEvent(input$switchPlot, {# Cycle through the plotscurrentPlot(switch(currentPlot(),"goal"="hfi","hfi"="grid","grid"="goal")) })# Render the appropriate plot output$distPlot <-renderPlot({if (currentPlot() =="goal") {ggplot(long_df_goal_distribution, aes(x = Value, y = Goal, fill =stat(x))) +geom_density_ridges_gradient(scale =3, size =0.3, rel_min_height =0.01) +scale_fill_viridis_c(name ="", option ="C") +theme(plot.title =element_text(hjust =0.5), plot.title.position ="plot") +labs(x ='Scores', y ='Goals', title ='SDG Goals Distribution') } elseif (currentPlot() =="hfi") {ggplot(long_df_hfi_distribution, aes(x = Value, y = Category, fill =stat(x))) +geom_density_ridges_gradient(scale =3, size =0.3, rel_min_height =0.01) +scale_fill_viridis_c(name ="", option ="C") +theme(plot.title =element_text(hjust =0.5), plot.title.position ="plot") +labs(x ='Scores', title ='Human Freedom Index Scores Distribution') } else {# Replace unempl_d, gdp_d, milit_d, internet_d with your ggplot objectsgrid.arrange(unempl_d, gdp_d, milit_d, internet_d, ncol =2, nrow =2) } })}# shinyApp(ui = ui, server = server)
Now, let’s display the distribution of the different SDG achievement scores per continent, using violin boxplots to have an overview of the mods, the range with most of the observations and the outliers.
Code
#### boxplots #####For sdg goals per continent #Africadata_Q1_Africa <- data_question1 %>%#filtering Africa as continentfilter(data_question1$continent =='Africa') %>% dplyr::select(continent, overallscore, goal1, goal2, goal3, goal4, goal5, goal6, goal7, goal8, goal9, goal10, goal11, goal12, goal13, goal15, goal16, goal17)data_Q1_Africa_long <-melt(data_Q1_Africa)medians_AF <- data_Q1_Africa_long %>%#median per variablegroup_by(variable) %>%summarize(median_value =median(value))data_Q1_Africa_long <- data_Q1_Africa_long %>%left_join(medians_AF, by ="variable")#Americadata_Q1_Americas <- data_question1 %>%#filtering Americas as continentfilter(data_question1$continent =='Americas') %>% dplyr::select(continent, overallscore, goal1, goal2, goal3, goal4, goal5, goal6, goal7, goal8, goal9, goal10, goal11, goal12, goal13, goal15, goal16, goal17)data_Q1_Americas_long <-melt(data_Q1_Americas)medians_AM <- data_Q1_Americas_long %>%#median per variablegroup_by(variable) %>%summarize(median_value =median(value))data_Q1_Americas_long <- data_Q1_Americas_long %>%left_join(medians_AM, by ="variable")#Asiadata_Q1_Asia <- data_question1 %>%filter(data_question1$continent =='Asia') %>%#filtering Asia as continent dplyr::select(continent, overallscore, goal1, goal2, goal3, goal4, goal5, goal6, goal7, goal8, goal9, goal10, goal11, goal12, goal13, goal15, goal16, goal17)data_Q1_Asia_long <-melt(data_Q1_Asia)medians_AS <- data_Q1_Asia_long %>%#median per variablegroup_by(variable) %>%summarize(median_value =median(value))data_Q1_Asia_long <- data_Q1_Asia_long %>%left_join(medians_AS, by ="variable")#Europedata_Q1_Europe <- data_question1 %>%filter(data_question1$continent =='Europe') %>%#filtering Europe as continent dplyr::select(continent, overallscore, goal1, goal2, goal3, goal4, goal5, goal6, goal7, goal8, goal9, goal10, goal11, goal12, goal13, goal15, goal16, goal17)data_Q1_Europe_long <-melt(data_Q1_Europe)medians_EU <- data_Q1_Europe_long %>%#median per variablegroup_by(variable) %>%summarize(median_value =median(value))data_Q1_Europe_long <- data_Q1_Europe_long %>%left_join(medians_EU, by ="variable")#Oceaniadata_Q1_Oceania <- data_question1 %>%filter(data_question1$continent =='Oceania') %>%#filtering Oceania as continent dplyr::select(continent, overallscore, goal1, goal2, goal3, goal4, goal5, goal6, goal7, goal8, goal9, goal10, goal11, goal12, goal13, goal15, goal16, goal17)data_Q1_Oceania_long <-melt(data_Q1_Oceania)medians_OC <- data_Q1_Oceania_long %>%#median per variablegroup_by(variable) %>%summarize(median_value =median(value))data_Q1_Oceania_long <- data_Q1_Oceania_long %>%left_join(medians_OC, by ="variable")# merge all mediansmedians_all <-rbind(data_Q1_Oceania_long, data_Q1_Americas_long,data_Q1_Africa_long,data_Q1_Asia_long,data_Q1_Europe_long)medians_all$color <-ifelse(medians_all$median_value >75, "lightgreen",ifelse(medians_all$median_value <25, "red3", 'lightblue3')) #assigning colors. If median for a goal is > 75 -> lightblue, if < 25 -> red, orange otherwise.bandwidth_nrd <-bw.nrd(medians_all$value) #adapting the bandwidthggplot(medians_all, aes(x = variable, y = value, fill = color)) +geom_violin(trim =FALSE, bw = bandwidth_nrd) +scale_fill_manual(values =c("lightgreen"="lightgreen", "red3"="red3", "lightblue3"="lightblue3"),labels =c("between", ">75", "<25")) +labs(title ="SDG Goals Distribution by Continent", x ="Goals", y ="Scores", fill ="Score Category") +facet_grid(continent ~ ., scales ="free_y") +scale_y_continuous(labels = scales::label_number()) +theme_classic() +theme(plot.title =element_text(hjust =0.5), # Center the titleplot.title.position ="plot", axis.text.x =element_text(angle =90, vjust =0.5, hjust=1))
Here is the distribution of the goals per continent. We notice that Europe the continent with most of its goals having a median superior to 75 (represented by the lightblue color. We notice that only two goals have a median score lower than 25, which is for goal 9 for Africa and goal 10 for America. As seen before, goal 9 is generally having lower scores than the other goals. That could mean that the access to technology and sustainable/resilient infrastructures/industrialization is harder in Africa, because of various reasons such as less wealthy countries, corruption,…
The goal 10 concerns the reduction of inequalities within/amongst countries. Therefore, we presume that less effort and investment has been made on this goal in America.
In addition, some distributions are quite disparsed, such as goal 13 in Oceania and goal 10 in Africa. That could again show inequalities within countries or less investment made to raise the scores by different countries of the same continent.
Now let’s display boxplots for the different variables of the human freedom index.
Here we can notice the same results as before concerning the SDG goals, except that no score has a median below 25%. Again, Europe is the continent with most of its median scores superior to 75 (lightblue color)
For space reason because of the different scales, we have decided not to make violin boxplot per continent for the remaining variables. The distribution can be seen in the general distribution seen prior to that.
Now, let’s have a closer look to the general correlation between our variables. Using our cleaned dataset, we will use a correlation heatmap to help us vizualising the informations. Given that most of our variables are not normally distributed, we will use the Spearman method to calculate the correlation.
Code
#### Correlations between variables Heatmap ####Correlation_overall <-data_question1 %>%# selection of the numerical data dplyr::select(population:ef_regulation)cor_matrix_sper <-# calculation of the correlation matrixcor(Correlation_overall, method ="spearman", use ="everything")cor_melted <-# wide to long transformationmelt(cor_matrix_sper)ggplot(data = cor_melted, aes(Var1, Var2, fill = value)) +geom_tile() +scale_fill_gradient2(low ="blue", high ="red", mid ="white", midpoint =0, limit =c(-1, 1), space ="Lab", name="Spearman\nCorrelation") +theme_minimal() +theme(axis.text.x =element_text(angle =45, vjust =1, size =6, hjust =1),axis.text.y =element_text(size =6),plot.title =element_text(hjust =0.5)) +coord_fixed() +labs(x ='', y ='', title ='Correlation Matrix Heatmap')
By looking at our heatmap, we notice that most of our goals are strongely correlated together and that some variables amongst the Human Freedom Index scores too (strong correlation among personal freedom variables (pf), reflecting scores from the Human Freedom Index on movement, religion, assembly, and expression). This could be explained by the fact that some of these goals/scores share partially similar objectifs, which could mean that a raise in the score of one of these goals will raise positively the score of another/some other goals. In addition, we notice that goals 12 and 13 (respectively “responsible consumption & production” and “climate action”) are strongely negatively correlated with most of our variables, except between themself.
We will see more in detail the correlations between our goals and variables in the analysis part of the influence of the factors over the Sustainable Development Goals.
In order to have an overview of the relationship between our independent variables and the SDG overall score, we make several graphs containing the Spearman correlation coefficient between the variable, the scatter plots describing the relationship between the variables, as well as the distribution of each variable. –>
Code
#### Spearman's correlation coeff ####lower.panel <-function(x, y, ...){points(x, y, pch =20, col ="darkgreen", cex =0.2)} panel.hist <-function(x, ...){ usr <-par("usr"); on.exit(par(usr))par(usr =c(usr[1:2], 0, 1.5) ) h <-hist(x, plot =FALSE) breaks <- h$breaks; nB <-length(breaks) y <- h$counts; y <- y/max(y)rect(breaks[-nB], 0, breaks[-1], y, col ="lightgreen", ...) }# panel.cor_stars function with stars alongside correlation coefficients panel.cor_stars <-function(x, y, digits =2, prefix ="", cex.cor, ...) { usr <-par("usr"); on.exit(par(usr))par(usr =c(0, 1, 0, 1)) r <-cor(x, y) p_value <-cor.test (x,y)$p.valueif (p_value <0.001){ stars <-"***" } elseif (p_value <0.01) { stars <-"**" } elseif (p_value <0.05) { stars <-"*" } else { stars <-"" } txt <-paste0(format(c(r, 0.123456789), digits = digits)[1], " ", stars)if(missing(cex.cor)) cex.cor <-0.5/strwidth(txt)text(0.5, 0.5, txt, cex = cex.cor) }# # Independent variablespairs(Correlation_overall[,c("overallscore", "unemployment.rate", "GDPpercapita", "MilitaryExpenditurePercentGDP", "internet_usage")], upper.panel=panel.cor_stars, diag.panel=panel.hist, lower.panel = lower.panel, main="Correlation table and distribution of various variables")# pairs(Correlation_overall[,c("overallscore", "pf_law", "pf_security", "pf_movement", "pf_religion", "pf_assembly" ,"pf_expression" ,"pf_identity", "ef_government", "ef_legal", "ef_money", "ef_trade", "ef_regulation")], upper.panel=panel.cor_stars, diag.panel=panel.hist, lower.panel = lower.panel, main="Correlation table and distribution of HFI variables")
Meaning of the stars: *** : p_value < 0.001; ** : p_value < 0.01; *: p_value <0.05; no star if p_value is higher.
The overall SDG achievement score is highly correlated with the percentage of people using the internet (r=.79) and GDP per capita (r=.60). The unemployement rate as well as the military expenditures in percentage of GDP per capita do not seem to play a role. However, this is only for the overall score.
The overall SDG achievement score is highly correlated with “personal freedom: law” (p=.69) and “personal freedom: identity” (p=.62). The other dimensions of personal freedom do not seem to have important influence. Regarding the distribution of the personal freedom variables, we notice that except for law, all have right-skewed distributions meaning that most of the countries have high scores.
The overall SDG achievement score is highly correlated with “economical freedom: legal” (p=.77), “economical trade: legal” (p=.67) and “economical freedom: money” (p=.6), while the other dimensions of economic freedom do not seem to have important influence. Regarding the distribution of the economic freedom variables, we notice more heterogeneous distributions and scores across the various countries than for personal freedom.
3.1.2 Analysis: analysis of the Influence of the factors over the Sustainable Development Goals
In order to answer the first question of our work, let’s start by zooming on the correlation matrix heatmap made in our EDA part. Here are the correlations between the SDG goals and all the other variables except the SDG goals.
Code
### Correlation Matrix Heatmap SDG/Other variables ####computing pvals of our interested variablescorr_matrix <-cor(data_question1[7:40], method ="spearman", use ="everything")p_matrix2 <-matrix(nrow =ncol(data_question1[7:40]), ncol =ncol(data_question1[7:40]))for (i in1:ncol(data_question1[7:40])) {for (j in1:ncol(data_question1[7:40])) { test_result <-cor.test(data_question1[7:40][, i], data_question1[7:40][, j]) p_matrix2[i, j] <- test_result$p.value }}corr_matrix[which(p_matrix2 >0.05)] <-NA#only keeping significant pval alpha = 0.05melted_corr_matrix_GVar <-melt(corr_matrix[19:34,1:18])ggplot(melted_corr_matrix_GVar, aes(Var1, Var2, fill = value)) +geom_tile() +geom_text(aes(label =ifelse(!is.na(value) &abs(value) >0.75, sprintf("%.2f", value), '')),color ="black", size =2) +scale_fill_gradient2(low ="blue", high ="red", mid ="white",midpoint =0, limit =c(-1, 1), space ="Lab",name ="Spearman\nCorrelation") +theme_minimal() +theme(axis.text.x =element_text(angle =45, hjust =1),axis.text.y =element_text(angle =45, hjust =1)) +labs(x ='Goals', y ='Goals',title ='Correlations Heatmap between goals and our other variables')
The numbers are representing the significant pval between our variables. The grey parts are the non significant pvals.
GDP per capita, internet_usage, pf_law or ef_legal are strongely correlated with most of our SDG goals. This is mostly due to the large scope englobed in these variables. That makes them influence various sectors of our economies and thus, mostly impacting all our SDG goals. Therefore, we can think that these variables have a strong impact on the scores. Nevertheless, as correlation doesn’t mean causality, we cannot jump to conclusions.
As we can see, our SDG goals 12 & 13 (responsible consumption & production and climate action) are negatively correlated with most of our variables, as is the economic freedom government variable to our SDG goals. Nevertheless, goals 12 & 13 and ef_government are positively correlated together.
Now let’s zoom on the correlations between all our variables except the SDG goals:
Code
melted_corr_matrix_Var <-melt(corr_matrix[19:34,19:34])ggplot(melted_corr_matrix_Var, aes(Var1, Var2, fill = value)) +geom_tile() +geom_text(aes(label =ifelse(!is.na(value) &abs(value) >0.75, sprintf("%.2f", value), '')),color ="black", size =1.7) +scale_fill_gradient2(low ="blue", high ="red", mid ="white",midpoint =0, limit =c(-1, 1), space ="Lab",name ="Spearman\nCorrelation") +theme_minimal() +theme(axis.text.x =element_text(angle =45, hjust =1),axis.text.y =element_text(angle =45, hjust =1)) +labs(x ='Goals', y ='Goals',title ='Correlations Heatmap between other variables than SDG goals')
As noticed earlier, there is a strong correlation among personal freedom variables (pf), reflecting scores from the Human Freedom Index on movement, religion, assembly, and expression.
Again, we can see that GDP per capita, pf_law, ef_legal are highly correlated with some other variables. On another hand, we notice that pf_movement, pf_assembly, pf_expression are now also higly correlated with some of the other variables.
In order to have a look at the influence of some factors over our dependent variables, let’s conduct a Principal Component Analysis over the Human Freedom Index Scores.
Now concerning the Human Freedom Index scores, most of the variables are positively correlated to the dimension 1, slightly less for the PF religion, and finally the EF government variable is slighlty incorrelated to the dimension 1. With a eigenvalue bigger than 1 for the three first components, we conclude that there are 3 dimensions to take into account. Nevertheless, again, they are explaining less than 80% of cumulated variance. Therefore, the rule of thumb would suggest us to take 4 dimensions into account.
Let’s try now to conduct a cluster analysis, using the Kmean method.
Code
data_kmean_country <- data_question1 %>% dplyr::select(-c(X,code,year,continent,region, population))#filter data different than 0 and dropping observations filtered_data <- data_kmean_country %>%group_by(country) %>%filter_if(is.numeric, all_vars(sd(.) !=0)) %>%ungroup()scale_by_country <- filtered_data %>%#scale datagroup_by(country) %>%summarise_all(~scale(.))means_by_country <- scale_by_country %>%#mean by countrygroup_by(country) %>%summarise_all(~mean(., na.rm =TRUE))rownames(means_by_country) <-seq_along(row.names(means_by_country))# Your existing elbow plotelbow_plot <-fviz_nbclust(means_by_country[,-1], kmeans, method="wss", linecolor ="steelblue")# Add a vertical line at the elbow point (4 clusters)elbow_plot_with_line <- elbow_plot +geom_vline(xintercept=4, linetype="dashed", color="red")print(elbow_plot_with_line)
After adapting the data for conducting our cluster analysis, we can see that according the the elbow method that we would only need 4 clusters in our analysis.
Our cluster analysis gives us one principal cluster (here in purple) –> CENTERED ON 0 BECAUSE AFTER DATA SCALED-> REALLY SMALL VALUES –> HOW TO DEAL WITH IT? I TRIED TO TAKE ONLY HFI INTO ACCOUNT BUT NOT WORKING NEITHER. STILL CENTERED ON 0.
While considering our regressions, we have noticed that we had high multicolinearity between our dependent variables in our models. This is due to the numerous variables that we tried to take into account while computing our regressions. Let’s find a model that could explain the overall SDG score without having severe multicollinearity (VIF > 5)
The model found is taking into account the following dependent variables: unemployment rate, military expenditure percentage of GDP, internet_usage, pf_security, pf_religion, pf_identity, ef_legal, ef_trade. We notice here that the previous variables highly correlated to the SDG goals (GDP per capita, pf_law, internet_usage and ef_legal), we dropped the first two ones.
As we can see, all of the variables above are significantly impacting the overall score of our Sustainable Development Goals. In addition, our Radjusted is high enough, which means that our model is well explained.
Code
##### geom point #####geom1 <-ggplot(data_question1, aes(internet_usage, overallscore)) +geom_point()+geom_smooth(se =FALSE) +labs(title ="Scarplot overallscore and internet usage")geom2 <-ggplot(data_question1, aes(unemployment.rate, overallscore)) +geom_point()+geom_smooth(se =FALSE) +labs(title ="Scarplot overallscore and unemployment rate")geom3 <-ggplot(data_question1, aes(MilitaryExpenditurePercentGDP,overallscore)) +geom_point()+geom_smooth(se =FALSE) +labs(title ="Scarplot overallscore and military expenditure")geom4 <-ggplot(data_question1, aes(pf_security,overallscore)) +geom_point()+geom_smooth(se =FALSE) +labs(title ="Scarplot overallscore and pf_security")geom5 <-ggplot(data_question1, aes(pf_religion, overallscore)) +geom_point()+geom_smooth(se =FALSE) +labs(title ="Scarplot overallscore and pf_religion")geom7 <-ggplot(data_question1, aes(pf_identity, overallscore)) +geom_point()+geom_smooth(se =FALSE) +labs(title ="Scarplot overallscore and pf_identity")geom8 <-ggplot(data_question1, aes(ef_legal, overallscore)) +geom_point()+geom_smooth(se =FALSE) +labs(title ="Scarplot overallscore and ef_legal")geom9 <-ggplot(data_question1, aes(ef_trade, overallscore)) +geom_point()+geom_smooth(se =FALSE) +labs(title ="Scarplot overallscore and ef_trade")grid.arrange(geom1, geom2, geom3, geom4, geom5, geom7, geom8, geom9, nrow=3, ncol=3)
By checking the influence of the chosen variables over the overallscore, we can see that the functions are not linear. For some, such as internet_usage and ef_legal, we notice that the more the variable increase, the more it influence positively the overall score. For the others, the relations are more complex. I.e.: Unemployment.rate increase mostly the overallscore between 0 and 10%. pf_identity is slowly reducing the overallscore before going back up.
In conclusion, after reviewing which variables are correlating between themself, after taking care of multicollinearity problems and doing our regressions on our overall SDG score and finally seeing the influence of these dependent variables depending on their range, we notice that most of our variables taken into account in our model is significant in explaining their influence (positive or negative) over the overall SDG goals. As our goals are mostly correlated between eachother, we can presume that taking the overall score as our dependent variable is giving us the same conclusion. Nevertheless, we still need to go deeper and check the influence of the scores between themself.
3.2 Focus on the relationships among the SDGs
How are the different SDGs linked? (We want to see if some SDGs are linked in the fact that a high score on one implies a high score on the other, and thus if we can make groups of SDGs that are comparable in that way).
3.2.1 EDA: General visualization of the SDGs
We want now to explore and analyse how the SDGs scores are linked together. We first, interest ourself to the correlation between the goals scores. To do that we chose to use a correlation heatmap. We set an arbitrary threashold to better concentrate our attention to the most corrolated goals. We fixed our threashold at 0.5 (indicating a strong positive relationship) and less than -0.5 (signifying a strong negative relationship).
Given that, as seen previously, our variables do not follow a normal distribution, employing the Pearson correlation method is not suitable in our analysis since it requires observations to be normaly distributed. We attempted to normalize the data through logarithmic or square root transformations, but these adjustments were insufficiently effective. Consequently, we will resort to computing the Spearman correlation. While not ideal, this method does not necessitate the normal distribution of our data. In our analysis, particularly for the heatmap visualization, we will focus on correlations that exceed the threshold of 0.5 or fall below -0.5. This selective approach will enhance the readability and interpretability of the heatmap.
To do that, we select only the colums of interest and compute the correlation matrix using Spearman correlation. We then melt the matrix to be able to plot it. We then plot the heatmap using ggplot2.
Code
#### Preparation of the data ##### Keeping only the columns of interest for the correlation calcluationdata_4_goals <- data_4 %>% dplyr::select(overallscore, goal1, goal2, goal3, goal4, goal5, goal6,goal7, goal8, goal9, goal10, goal11, goal12, goal13, goal15, goal16, goal17)
Code
#### Spearman Correlation ##### Calculate Spearman correlationspearman_corr_4 <-cor(data_4_goals, method ="spearman", use ="everything")# Apply threshold and replace values below it with NAspearman_corr_4[abs(spearman_corr_4) < threashold_heatmap] <-NA
Code
#### Spearman Correlation Heatmap ##### Melting the datamelted_corr_4 <-melt(spearman_corr_4, na.rm =TRUE)# Creation of the heatmapggplot(data = melted_corr_4, aes(x = Var1, y = Var2, fill = value)) +geom_tile() +geom_text(aes(label =sprintf("%.2f", value)), vjust =0.5, size=2.5) +# Adding textscale_fill_gradient2(low ="blue", high ="red", mid ="white", midpoint =0, limit =c(-1,1), space ="Lab", name="Spearman\nCorrelation",na.value ="grey") +theme_minimal() +theme(axis.text.x =element_text(angle =45, hjust =1)) +labs(title ="Heatmap of Spearman Correlations for Goals", x ="", y ="")
The correaltion can be read on the graph. The darker the color, the stronger the correlation. If there is not colors, it means that the gaols correlation does not exceed our threashold of ±0.5.
It is evident that the Sustainable Development Goals (SDGs) are intricately interconnected. However, certain goals appear to be less interrelated compared to others. Specifically, SDG 1 (No Poverty) and SDG 10 (Reduced Inequalities) demonstrate a weaker correlation with the rest of the goals. Similarly, Goal 15 (Life on Land) also exhibits a lesser degree of interconnection with the other SDGs. It is also interesting to note that some goals are negatively correlated with others. For instance, based on the Spearman correlation, goal 12 (Responsable Consumption and Production) and goal 13 (Climate Action) are negatively correlated with the others goals. This suggest that when the higher a goal other than goal 12 or 13 is, the lower the goals 12 and 13 are. Given their similar nature, it is not surprising that they are highly correlated with each other.
3.2.2 Analysis: Factor analysis and Stepwise regression applied to the SDGs
At this point, we saw that the goals were mostly correlated. We now want to see if we can group them in a smaller number of factors. To do that, we will use a principal component analysis (PCA). We will first look at the scree plot to see how many factors we should keep. We will then look at the biplot to see how the goals are grouped together.
Code
#### Scree Plot ##### Selecting only the goals columnsgoals_data <- data_4 %>% dplyr::select(goal1, goal2, goal3, goal4, goal5, goal6,goal7, goal8, goal9, goal10, goal11, goal12, goal13, goal15, goal16, goal17)goals_data_scaled <-scale(goals_data) # Scaling the datapca_result <-prcomp(goals_data_scaled) # Running PCA# Plotting Scree plot to visualize the importance of each principal componentfviz_eig(pca_result,addlabels =TRUE,col.var="dodgerblue3") +theme_minimal()eigenvalues <- pca_result$sdev^2# getting the eigenvalues
We see clearly that the first component is the most important one. Guided by the Kaiser criterion, which advises retaining only those components with eigenvalues exceeding 1, the initial three components emerge as candidates. However, considering the third component’s eigenvalue of 1.016, we opted for simplification by focusing exclusively on the first two components even though the third eigenvalue is technically higher than 1. This decision also enhances clarity in the biplot representation, as it reduces the dimensions to just two, making interpretation more straightforward.
Code
#### Biplot ##### Plotting Biplot to visualize the two main dimensionsfviz_pca_biplot(pca_result,label="var",col.var="dodgerblue3",geom="point",pointsize =0.1,labelsize =4) +theme_minimal()
The biplot offers insightful visualization, clearly illustrating the relationship between the various goals and the first two components. Notably, Dimension 2 exhibits a strong correlation with Goals 10 (Reduced inequalities) and 15 (Life on Land), whereas the remaining goals show a moderate to high correlation with Dimension 1. Furthermore, an interesting pattern emerges, revealing three distinct groups of variables, each playing a unique role. One group comprises Goals 12 (Responsible Consumption and Production) and 13 (Climate Action), another encompasses Goals 10 (Reduced inequalities) and 15 (Life on Land), and the third group includes all other variables. This categorization aids in understanding the distinct influences and interactions among the goals.
Grouping Goal 12 (Responsible Consumption and Production) and Goal 13 (Climate Action) together is logical, as both pertain to environmental issues. It is, however, interesting to note the pairing of Goal 10 (Reduced Inequalities) with Goal 15 (Life on Land). This could be explained by the fact that Goal 10 (Reduced inequalities) is related to the reduction of inequalities within and among countries, while Goal 15 (Life on Land) is related to the protection, restoration and promotion of sustainable use of terrestrial ecosystems, sustainable manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss. Therefore, it is possible that the respondents who are more concerned about the reduction of inequalities are also more concerned about the protection of the environment. But this is a stretched.
3.3 Focus on the evolution of SDG scores over time
How has the adoption of the SDGs in 2015 influenced the achievement of SDGs?
We create one new variable per goal that captures the difference in SDG score between the year of the observation and the previous year. This will allow us to see how the countries improve (or not) on SDG scores each year.
3.3.1 EDA: General time evolution of SDG socres
First, we look at the evolution of SDG achievement overall score over time by continent and by region and we see that the general evolution of SDG scores around the world is increasing over the years, but very slowly. We also plot the average improvements/decrease in overall score acros the years. Looking at the continents, we see that Europe is above the others, while Africa is below, but in general, all have increasing overall scores. In addition, we see that the different continent have quite steady score difference between the years, except Oceania that has greater fluctuations and has sometimes score decrease, for instance in 2010. But it also comes with the higher average increase in 2014. We also observe that toward the latest years (2021-2022), the improvements are smaller and that decrease are more frequent. However, we must keep in mind that all are very small differences, indeed, the higher improvement is a little above 1 point of percentage of the overall score in one year.
<<<<<<< Updated upstream
=======
>>>>>>> Stashed changes
This view that groups the countries by region gives us precision about the previous information. Indeed, it is Western Europe that is particularly above and Sub-Saharan Africa that is clearly below. Regarding the score difference from one year to another, we still see Oceania having the greater fluctuations, but Caucasus & Central Asia as well as Eastern Europe show a pick in the early two thousand’s that we could not see before. South Asia also has a relatively high improvement in 2017.
<<<<<<< Updated upstream
=======
>>>>>>> Stashed changes
Second, we look at the evolution of SDG achievement scores(16) over time for the whole world and by continent. We notice that all SDGs except from goal 9 (industry innovation and infrastructure) are close to one another in terms of level and growth. Goal 9 starts far below the others in 2000 and growths faster until exceeding 50%. In addition, some goals did not increase their scores much in the last two decades, for example goal 13 (climate action) and goal 12 (responsible consumption and production). The score differences are mostly contained between 0 and 0.5 point of percentage increase by year. Some of the goals have picks, these are goals 15, 3, 5, 17 and goal 9 has the highest of all average improvements in 2017 with an improvement of 4 points of percentage, which is almost two times higher than the other good improvements. Some goals have bad years, like goal 10 or goal 15, but never under -0.5, except goal 16 in 2022 that goes a little below. Finaly, some goals are very steady, for example goal 12 that stays around zero and goal 6 that is always a little above the zero (no change) line.
<<<<<<< Updated upstream
=======
>>>>>>> Stashed changes
We continue with the graph that distinguishes continents to get more information.
<<<<<<< Updated upstream
=======
>>>>>>> Stashed changes
We observe that most of the time, Europe is at the top of the graph and Africa at the bottom, except for goals 12 and 13 that are linked to ecology. Some other information stand out:
Americas are far behind the other parts of the world regarding goal 10: reduced inequalities.
Africa is far behind the other continents (even if becoming better) for goals 1, 3, 4 and 7.
Goal 9 (industry, innovation and infrastructure) show exponential growth for almost all continents.
Third we create an interactive map of the world to be able to navigate from year 2000 to 2022, seeing the level of achievement of the SDGs (overall score) for each country.
Again, we see that the overall achievement score of the SDGs is increasing and that the countries that have the most red (bad score) are in Africa. However it is also there that it increases more rapidly. Our hypothesis is that when a score is very low, it is easier to make it better than when it becomes very high (around 90%) it may be hard to increase it, because it would mean perfection. In the next section, we will further investigate this idea.
3.3.2 Analysis: SDG adoption in 2015
Preparing for the specific question around 2015, we only keep the years from 2009 to 2022 (7 years before and after 2015).In addition, we create a binary variable that take the value 1 if the observation occurred after 2015 and zero otherwise.
We begin by looking at the distribution of the difference in SDG scores from one year to the next (improvement if it is above zero and deterioration if it is below zero).
We notice that across the years, the distribution stays on the right of the x-axis, which means that there are more improvement than deterioration. If there is deterioration, it is less than one percent per year, except some extreme cases, for instance in 2013, there was almost a 3% decrease in the overall SDG score of one country. It is also rare to see improvements of more than 2% per year. Regarding our specific question, we do not see a major improvement of the distribution after 2015, if it was the case we would see the distribution going more to the right, but except for 2017, there are more and more values centered around zero, which means less score improvements overall.
After having visualized the improvements and declines of SDG overall score for the whole world, we are now interested in the top 5 countries in terms of improvement each year and we see that major improvement often comes from Sub-Saharan Africa countries or Middle East and North Africa. This confirms that more efforts are made in these regions to achieve better scores, but we also know from our previous visualizations that their initial scores are lower. Moreover, we record that the higher improvements are of 3% per year and were mostly achieved before 2015. Indeed, we can tell that in terms of maximum improvements, the adoption of SDGs in 2015 did not have a strong impact. We also notice that 2020 is the year with the smallest best improvements. We keep that in mind for the next question regarding events and specifically COVID.
We continue by looking at the worst 5 countries in terms of decline in SDG overall score each year and we see that the years with the worst declines are those closer to us. Indeed the declines were generally no more than 1%, until 2018, where these became more frequent. We notice that the adoption of SDGs in 2015 may have had a good impact, because during the two years that follow, the worst SDG score declines were low (no more than 1% in 2016 and no more 0.5% in 2017). It was stabilizing, but it was of short duration, because then come the more extreme deteriotations. Interestingly, the regions that had were the worst in terms of decline during the past twelve years were very different, the only pattern appears during the last four years, where most of them are in Latin America and the Caribbean.
We move on to the specific SDG scores and look at the 20 best improvements by score. We additionaly differentiate between the improvements than occurred before and after 2015. We want to see which goals get the best improvements and which countries put more effort into it.
Goals 2 (zero hunger), 3 (good health and well-being), 6 (clean water and sanitation), 8 (decent work and economic growth), 12 (responsible consumption and production), 16 (peace, justice and strong institutions) have very low improvements per year. Indeed, even the best ones are below 10%.
Goal 10 (reduced inequalities) has the best improvements, all 20 best improvements are above 20% and it goes up to 45%.
Some goals clearly had most of their best improvements before 2015: goals 3 (good health and well-being), 5 (gender equality), 6 (clean water and sanitation), 7 (affordable and clean energy).
Some goals clearly had most of their best improvements after 2015: goals 8 (decent work and economic growth), 12 (responsible consumption and production).
Goal 9 (industry, innovation and infrastructure) has all of its 20 best improvements after 2015.
Regarding the impact of the adoption of SDGs in 2015, we can not tell that it had a positive impact, because there are not more big improvements after 2015 than before, even a little bit less. In addition, the most impressive improvements mostly occurred before 2015. These conclusions are supported by the next graph: we fit two different regression lines (before and after 2015) to see if there is a jump after the adoption of the SDGs and if the the SDG scores increase faster. We decided to cut the y-axis in order to have a better visual of the different scores. Since the regressions lines (taking into account all of the goals) go between 30% and 85% we only kept those values.
Goals 1, 4, 3, and 15 increase faster before 2015 than after.
Except for goal 17, none seem to increase faster after the adoption of SDGs. Since goal 17 is about collaboration of the countries for SDGs achievement, it is no surprise that before the adoption, there were no increase. It is thus disappointing to see that it is the only goal that has a improvement rate after 2015.
Goal 17 also has a small downward jump in 2015, but since it immediately increases in the following years, it is due to the fitting of the lines.
We observe small upwards jumps for goals 8, 9, 10 and 11.
To sum up, the adoption of SDGs was a success in terms of collaboration between countries to better themselves on some aspects of durability (goal 17), but regarding the goals themselves, we can not conclude to faster improvements or radical efforts following 2015.
3.4 Focus on the influence of events over the SDG scores
In order to have an overview of the relationship between the different events variables and the SDG overall score, we make several graphs containing the Pearson correlation coefficient between the variable, the scatter plots describing the relationship between the variables, as well as the distribution of each variable.
Code
<<<<<<< Updated upstream
lower.panel <-function(x, y, ...){points(x, y, pch =20, col ="darkgreen", cex =0.2)} panel.hist <-function(x, ...){ usr <-par("usr"); on.exit(par(usr))par(usr =c(usr[1:2], 0, 1.5) ) h <-hist(x, plot =FALSE) breaks <- h$breaks; nB <-length(breaks) y <- h$counts; y <- y/max(y)rect(breaks[-nB], 0, breaks[-1], y, col ="lightgreen", ...) }# panel.cor_stars function with stars alongside correlation coefficients panel.cor_stars <-function(x, y, digits =2, prefix ="", cex.cor, ...) { usr <-par("usr"); on.exit(par(usr))par(usr =c(0, 1, 0, 1)) r <-cor(x, y) p_value <-cor.test (x,y)$p.valueif (p_value <0.001){ stars <-"***" } elseif (p_value <0.01) { stars <-"**" } elseif (p_value <0.05) { stars <-"*" } else { stars <-"" } txt <-paste0(format(c(r, 0.123456789), digits = digits)[1], " ", stars)if(missing(cex.cor)) cex.cor <-0.5/strwidth(txt)text(0.5, 0.5, txt, cex = cex.cor) }pairs(data_question3_1[, c("overallscore", "total_affected", "total_deaths")], upper.panel = panel.cor_stars,diag.panel = panel.hist,lower.panel = lower.panel, main ="Correlation table and distribution of Disaster variables")
=======
lower.panel <-function(x, y, ...){points(x, y, pch =20, col ="darkgreen", cex =0.2)} panel.hist <-function(x, ...){ usr <-par("usr"); on.exit(par(usr))par(usr =c(usr[1:2], 0, 1.5) ) h <-hist(x, plot =FALSE) breaks <- h$breaks; nB <-length(breaks) y <- h$counts; y <- y/max(y)rect(breaks[-nB], 0, breaks[-1], y, col ="lightgreen", ...) }# panel.cor_stars function with stars alongside correlation coefficients panel.cor_stars <-function(x, y, digits =2, prefix ="", cex.cor, ...) { usr <-par("usr"); on.exit(par(usr))par(usr =c(0, 1, 0, 1)) r <-cor(x, y) p_value <-cor.test (x,y)$p.valueif (p_value <0.001){ stars <-"***" } elseif (p_value <0.01) { stars <-"**" } elseif (p_value <0.05) { stars <-"*" } else { stars <-"" } txt <-paste0(format(c(r, 0.123456789), digits = digits)[1], " ", stars)if(missing(cex.cor)) cex.cor <-0.5/strwidth(txt)text(0.5, 0.5, txt, cex = cex.cor) }pairs(data_question3_1[, c("overallscore", "total_affected", "total_deaths")], upper.panel = panel.cor_stars,diag.panel = panel.hist,lower.panel = lower.panel, main ="Correlation table and distribution of Disaster variables")
>>>>>>> Stashed changes
Meaning of the stars: *** : p_value < 0.001; ** : p_value < 0.01; *: p_value <0.05; no star if p_value is higher.
The different variables used to materialize the impact of climate disasters do not seem to have important influence on the overall score. Indeed, the overallscore and total_affected have a correlation coefficient that suggests a very weak negative linear relationship between this variables and which is not statistically significant (p ≥ 0.05), and the overallscore and total_deaths have a correlation that also indicates a weak negative linear relationship that is statistically significant at p < 0.05. But we will further explore for the different SDGs, since we believe that such disasters have a specific influence on some SDGs.
<<<<<<< Updated upstream
Code
pairs(data_question3_2[,c("overallscore", "cases_per_million", "deaths_per_million", "stringency")], upper.panel = panel.cor_stars, diag.panel=panel.hist, lower.panel = lower.panel,main="Correlation table and distribution of COVID variables")
=======
Code
pairs(data_question3_2[,c("overallscore", "cases_per_million", "deaths_per_million", "stringency")], upper.panel = panel.cor_stars, diag.panel=panel.hist, lower.panel = lower.panel,main="Correlation table and distribution of COVID variables")
>>>>>>> Stashed changes
Meaning of the stars: *** : p_value < 0.001; ** : p_value < 0.01; *: p_value <0.05; no star if p_value is higher.
The different variables used to materialize the impact of COVID19 do not seem to have important influence on the overall score, we can see that Overallscore and cases_per_million/deaths_per_million/stringency have a correlation coefficient indicating a weak positive linear relationship that is highly statistically significant at p < 0.001. But we will further explore for the different SDGs, since we believe that COVID19 had a specific influence on some SDGs, for instance “good health and well-being” or “decent work and economic growth”.
Concerning the correlation effect between the COVID19 variables, we could have no surprises, Cases_per_million and deaths_per_million have a moderate to strong positive correlation suggesting a stronger relationship where an increase in the number of COVID-19 cases per million is associated with a substantial increase in the number of deaths per million. This indicates a significant correlation between case prevalence and mortality rate. Cases_per_million and stringency have a moderate positive correlation indicates that higher levels of cases per million are associated with slightly higher severity of health measures. This could mean that in regions where cases are more numerous, stricter sanitary measures can be put in place to control the spread of the virus. Finally, Deaths_per_million and stringency have a strong positive correlation indicating a robust relationship where higher mortality rates are associated with higher severity of sanitary measures. This suggest that in areas where deaths are higher, stricter sanitary measures are applied in an attempt to reduce the spread of the virus and mortality.
<<<<<<< Updated upstream
Code
pairs(data_question3_3[,c("overallscore", "ongoing", "sum_deaths", "pop_affected", "area_affected", "maxintensity")], upper.panel = panel.cor_stars, diag.panel=panel.hist, lower.panel = lower.panel, main="Correlation table and distribution of conflicts variables")
=======
Code
pairs(data_question3_3[,c("overallscore", "ongoing", "sum_deaths", "pop_affected", "area_affected", "maxintensity")], upper.panel = panel.cor_stars, diag.panel=panel.hist, lower.panel = lower.panel, main="Correlation table and distribution of conflicts variables")
>>>>>>> Stashed changes
Meaning of the stars: *** : p_value < 0.001; ** : p_value < 0.01; *: p_value <0.05; no star if p_value is higher.
Negative values (ranging from -0.17 to -0.28) with three stars (***) indicate a strong and statistically significant negative correlation between the overall index (Overallscore) and the various conflict-related variables (Ongoing, sum_deaths, pop_affected, area_affected, maxintensity). A strong negative correlation means that an increase in the Overallscore is associated with a decrease in the values of the other variables. But we have to take into account that correlation does not necessarily imply direct causation. Further analysis may be required to understand in depth the nature of the relationships between these variables.
To explore our data on events such as disasters, covid-19 and conflicts we have to first see which countries are the most touched by these. To do so, we made time-series analysis on this three events each time depending on different variables.
<<<<<<< Updated upstream
Code
# Converted 'year' column to date formatQ3.1$year <-as.Date(as.character(Q3.1$year), format ="%Y")Q3.2$year <-as.Date(as.character(Q3.2$year), format ="%Y")Q3.3$year <-as.Date(as.character(Q3.3$year), format ="%Y")
These is our time-analysis concerning the COVID-19 cases per million by region between end 2018 and 2022.
Code
covid_filtered <- Q3.2[Q3.2$year >=as.Date("2018-12-12"), ]ggplot(data = covid_filtered, aes(x = year, y = cases_per_million, group = region, color = region)) +geom_smooth(method ="loess", se =FALSE, span =0.8, size =0.6) +labs(x ="Year", y ="Cases per Million") +facet_wrap(~ region, ncol =3, strip.position ="top") +scale_y_continuous(labels =function(x) format(x, scientific =FALSE)) +theme(axis.text.x =element_text(angle =45, size =8, hjust =1),axis.text.y =element_text(vjust =1, size =8, hjust =1),strip.text =element_text(size =8),plot.title =element_text(hjust =0.5),panel.spacing =unit(1, "lines"),legend.position ="none" ) +ggtitle("Trend of COVID-19 Cases per Million Over Time")
=======
Code
# Converted 'year' column to date formatQ3.1$year <-as.Date(as.character(Q3.1$year), format ="%Y")Q3.2$year <-as.Date(as.character(Q3.2$year), format ="%Y")Q3.3$year <-as.Date(as.character(Q3.3$year), format ="%Y")
These is our time-analysis concerning the COVID-19 cases per million by region between end 2018 and 2022.
Code
covid_filtered <- Q3.2[Q3.2$year >=as.Date("2018-12-12"), ]ggplot(data = covid_filtered, aes(x = year, y = cases_per_million, group = region, color = region)) +geom_smooth(method ="loess", se =FALSE, span =0.8, size =0.6) +labs(x ="Year", y ="Cases per Million") +facet_wrap(~ region, ncol =3, strip.position ="top") +scale_y_continuous(labels =function(x) format(x, scientific =FALSE)) +theme(axis.text.x =element_text(angle =45, size =8, hjust =1),axis.text.y =element_text(vjust =1, size =8, hjust =1),strip.text =element_text(size =8),plot.title =element_text(hjust =0.5),panel.spacing =unit(1, "lines"),legend.position ="none" ) +ggtitle("Trend of COVID-19 Cases per Million Over Time")
>>>>>>> Stashed changes
<<<<<<< Updated upstream
=======
>>>>>>> Stashed changes
These is our time-analysis concerning the COVID-19 deaths per million per region between end 2018 and 2022
<<<<<<< Updated upstream
Code
ggplot(data = covid_filtered, aes(x = year, y = deaths_per_million, group = region, color = region)) +geom_smooth(method ="loess", se =FALSE, span =0.8, size =0.6) +labs(x ="Year", y ="Deaths per Million") +facet_wrap(~ region, nrow =5, strip.position ="top") +theme(axis.text.x =element_text(angle =45, size =8, hjust =1),axis.text.y =element_text(vjust =1, size =8, hjust =1),strip.text =element_text(size =8),panel.spacing =unit(0.5, "lines"),plot.title =element_text(hjust =0.5),legend.position ="none" ) +ggtitle("Trend of COVID-19 Deaths per Million Over Time")
=======
Code
ggplot(data = covid_filtered, aes(x = year, y = deaths_per_million, group = region, color = region)) +geom_smooth(method ="loess", se =FALSE, span =0.8, size =0.6) +labs(x ="Year", y ="Deaths per Million") +facet_wrap(~ region, nrow =5, strip.position ="top") +theme(axis.text.x =element_text(angle =45, size =8, hjust =1),axis.text.y =element_text(vjust =1, size =8, hjust =1),strip.text =element_text(size =8),panel.spacing =unit(0.5, "lines"),plot.title =element_text(hjust =0.5),legend.position ="none" ) +ggtitle("Trend of COVID-19 Deaths per Million Over Time")
>>>>>>> Stashed changes
<<<<<<< Updated upstream
=======
>>>>>>> Stashed changes
These is our time-analysis concerning the COVID-19 stringency per region between end 2018 and 2022
<<<<<<< Updated upstream
Code
ggplot(data = covid_filtered, aes(x = year, y = stringency, group = region, color = region)) +geom_smooth(method ="loess", se =FALSE, span =0.7, size =0.6) +labs(x ="Year", y ="Stringency") +facet_wrap(~ region, nrow =5) +theme(axis.text.x =element_text(angle =45, size =8, hjust =1),axis.text.y =element_text(vjust =1, size =8, hjust =1),strip.text =element_text(size =8),panel.spacing =unit(0.5, "lines"),plot.title =element_text(hjust =0.5),legend.position ="none" ) +ggtitle("Trend of COVID-19 Stringency Over Time")
=======
Code
ggplot(data = covid_filtered, aes(x = year, y = stringency, group = region, color = region)) +geom_smooth(method ="loess", se =FALSE, span =0.7, size =0.6) +labs(x ="Year", y ="Stringency") +facet_wrap(~ region, nrow =5) +theme(axis.text.x =element_text(angle =45, size =8, hjust =1),axis.text.y =element_text(vjust =1, size =8, hjust =1),strip.text =element_text(size =8),panel.spacing =unit(0.5, "lines"),plot.title =element_text(hjust =0.5),legend.position ="none" ) +ggtitle("Trend of COVID-19 Stringency Over Time")
>>>>>>> Stashed changes
<<<<<<< Updated upstream
=======
>>>>>>> Stashed changes
These is our time-analysis concerning climatic disasters with total affected per region
<<<<<<< Updated upstream
Code
Q3.1[is.na(Q3.1)] <-0ggplot(data = Q3.1, aes(x = year, y = total_affected, group = region, color = region)) +geom_smooth(method ="loess", se =FALSE, span =0.7, size =0.5) +labs(x ="Year", y ="Total Affected") +facet_wrap(~ region, nrow =5) +scale_y_continuous(labels =function(x) format(x, scientific =FALSE)) +theme(axis.text.x =element_text(angle =45, size =8, hjust =1),axis.text.y =element_text(vjust =1, size =8, hjust =1),strip.text =element_text(size =8),panel.spacing =unit(0.5, "lines"),plot.title =element_text(hjust =0.5),legend.position ="none" ) +ggtitle("Trend of Total Affected from Climatic Disasters Over Time")
=======
Code
Q3.1[is.na(Q3.1)] <-0ggplot(data = Q3.1, aes(x = year, y = total_affected, group = region, color = region)) +geom_smooth(method ="loess", se =FALSE, span =0.7, size =0.5) +labs(x ="Year", y ="Total Affected") +facet_wrap(~ region, nrow =5) +scale_y_continuous(labels =function(x) format(x, scientific =FALSE)) +theme(axis.text.x =element_text(angle =45, size =8, hjust =1),axis.text.y =element_text(vjust =1, size =8, hjust =1),strip.text =element_text(size =8),panel.spacing =unit(0.5, "lines"),plot.title =element_text(hjust =0.5),legend.position ="none" ) +ggtitle("Trend of Total Affected from Climatic Disasters Over Time")
We can see that the regions the most affected by the conflicts are East Asia, North America and South Asia. We weill concetrate more in this regions.
These is our time-analysis concerning conflicts deaths per region between 2000 and 2016
Code
conflicts_filtered <- Q3.3[Q3.3$year >=as.Date("2000-01-01") & Q3.3$year <=as.Date("2016-12-31"), ]ggplot(data = conflicts_filtered, aes(x = year, y = sum_deaths, group = region, color = region)) +geom_smooth(method ="loess", se =FALSE, span =0.3, size =0.6) +labs(x ="Year", y ="Sum 0f Deaths") +facet_wrap(~ region, nrow =5) +theme(axis.text.x =element_text(angle =45, size =8, hjust =1),axis.text.y =element_text(vjust =1, size =8, hjust =1),strip.text =element_text(size =8),panel.spacing =unit(0.5, "lines"),plot.title =element_text(hjust =0.5),legend.position ="none" ) +ggtitle("Trend of Deaths by Conflicts Over Time")
>>>>>>> Stashed changes
<<<<<<< Updated upstream
We can see that the regions the most affected by the conflicts are East Asia, North America and South Asia. We weill concetrate more in this regions.
These is our time-analysis concerning conflicts deaths per region between 2000 and 2016
Code
conflicts_filtered <- Q3.3[Q3.3$year >=as.Date("2000-01-01") & Q3.3$year <=as.Date("2016-12-31"), ]ggplot(data = conflicts_filtered, aes(x = year, y = sum_deaths, group = region, color = region)) +geom_smooth(method ="loess", se =FALSE, span =0.3, size =0.6) +labs(x ="Year", y ="Sum 0f Deaths") +facet_wrap(~ region, nrow =5) +theme(axis.text.x =element_text(angle =45, size =8, hjust =1),axis.text.y =element_text(vjust =1, size =8, hjust =1),strip.text =element_text(size =8),panel.spacing =unit(0.5, "lines"),plot.title =element_text(hjust =0.5),legend.position ="none" ) +ggtitle("Trend of Deaths by Conflicts Over Time")
=======
>>>>>>> Stashed changes
We can see that the regions the most affected by the conflicts are : Middle east and north Africa, Sub-Saharan Africa, South Asia, then very less America & the Caribbean and Eastern Europe.
These is our time-analysis concerning conflicts affected population per region between 2000 and 2016.
<<<<<<< Updated upstream
Code
ggplot(data = conflicts_filtered, aes(x = year, y = pop_affected, group = region, color = region)) +geom_smooth(method ="loess", se =FALSE, span =0.3, size =0.6) +labs(x ="Year", y ="Population affected") +facet_wrap(~ region, nrow =5) +theme(axis.text.x =element_text(angle =45, size =8, hjust =1),axis.text.y =element_text(vjust =1, size =8, hjust =1),strip.text =element_text(size =8),panel.spacing =unit(0.5, "lines"),plot.title =element_text(hjust =0.5),legend.position ="none" ) +ggtitle("Trend of Population Affected by Conflicts Over Time")
=======
Code
ggplot(data = conflicts_filtered, aes(x = year, y = pop_affected, group = region, color = region)) +geom_smooth(method ="loess", se =FALSE, span =0.3, size =0.6) +labs(x ="Year", y ="Population affected") +facet_wrap(~ region, nrow =5) +theme(axis.text.x =element_text(angle =45, size =8, hjust =1),axis.text.y =element_text(vjust =1, size =8, hjust =1),strip.text =element_text(size =8),panel.spacing =unit(0.5, "lines"),plot.title =element_text(hjust =0.5),legend.position ="none" ) +ggtitle("Trend of Population Affected by Conflicts Over Time")
We can see that the regions the most affected by the conflicts are : Middle east and north Africa, Sub-Saharan Africa, South Asia, Latin America & the Caribbean and Caucasus and Central Asia.
Finally, these is our time-analysis concerning maxintensity conflicts per region between 2000 and 2016. ::: {.cell layout-align=“center” hash=‘report_cache/html/unnamed-chunk-266_eee419880253939f1bfec2f300238f10’}
Code
ggplot(data = conflicts_filtered, aes(x = year, y = maxintensity, group = region, color = region)) +geom_smooth(method ="loess", se =FALSE, span =0.3, size =0.6) +labs(x ="Year", y ="Maxintensity") +facet_wrap(~ region, nrow =5) +theme(axis.text.x =element_text(angle =45, size =8, hjust =1),axis.text.y =element_text(vjust =1, size =8, hjust =1),strip.text =element_text(size =8),panel.spacing =unit(0.5, "lines"),plot.title =element_text(hjust =0.5),legend.position ="none" ) +ggtitle("Trend of Maxintensity by Conflicts Over Time")
>>>>>>> Stashed changes
<<<<<<< Updated upstream
We can see that the regions the most affected by the conflicts are : Middle east and north Africa, Sub-Saharan Africa, South Asia, Latin America & the Caribbean and Caucasus and Central Asia.
Finally, these is our time-analysis concerning maxintensity conflicts per region between 2000 and 2016. ::: {.cell layout-align=“center” hash=‘report_cache/html/unnamed-chunk-161_df201fdb8578b0eff63f5f30230ff3bc’}
Code
ggplot(data = conflicts_filtered, aes(x = year, y = maxintensity, group = region, color = region)) +geom_smooth(method ="loess", se =FALSE, span =0.3, size =0.6) +labs(x ="Year", y ="Maxintensity") +facet_wrap(~ region, nrow =5) +theme(axis.text.x =element_text(angle =45, size =8, hjust =1),axis.text.y =element_text(vjust =1, size =8, hjust =1),strip.text =element_text(size =8),panel.spacing =unit(0.5, "lines"),plot.title =element_text(hjust =0.5),legend.position ="none" ) +ggtitle("Trend of Maxintensity by Conflicts Over Time")
=======
>>>>>>> Stashed changes
::: We can see that the regions having a maxintensity in conflicts are : Caucasus and Central Asia, Eastern Europe, Middle east and north Africa, Sub-Saharan Africa, North America, South Asia, East Asia just at one precise moment, Latin America & the Caribbean less.
Now that we could visualize which regions are the most impacted by these three events we can do correlations analysis per region to see if this events have indeed an impact on the evolution of SDG goals.
3.5 Focus on the correlation between the SDG scores and the different events.
Here you can see an extract of our correlation map between the climate disasters and the SDG goals in South and East Asia and North America as it was the regions that where the most impacted. We conclude that climate disasters do not really have a big impact on SDG scores per region.
<<<<<<< Updated upstream
Code
library(ggplot2)disaster_data <- Q3.1[Q3.1$region %in%c("South Asia", "East Asia", "North America"), ]relevant_columns <-c("goal1", "goal2", "goal3", "goal4", "goal5", "goal6", "goal7", "goal8", "goal9", "goal10", "goal11", "goal12", "goal13", "goal15", "goal16", "total_affected", "total_deaths")subset_data <- disaster_data[, relevant_columns]correlation_matrix_subset <-cor(subset_data[, c("total_affected", "total_deaths")], subset_data)cor_melted <- reshape2::melt(correlation_matrix_subset)names(cor_melted) <-c("Variable2", "Variable1", "Correlation")ggplot(data = cor_melted, aes(Variable1, Variable2, fill = Correlation)) +geom_tile() +scale_fill_gradient2(low ="blue", high ="red", mid ="white",midpoint =0, limit =c(-1, 1), space ="Lab",name ="Correlation") +theme_minimal() +theme( axis.text.x =element_text(angle =45, size =8, hjust =1),axis.text.y =element_text(vjust =1, size =8, hjust =1),plot.title =element_text(margin =margin(b =20), hjust =0.5, vjust =8, lineheight =1.5) ) +coord_fixed() +labs(x ='', y ='',title ='Correlation between the climate disasters and the SDG goals in South and East Asia and North America')
=======
Code
library(ggplot2)disaster_data <- Q3.1[Q3.1$region %in%c("South Asia", "East Asia", "North America"), ]relevant_columns <-c("goal1", "goal2", "goal3", "goal4", "goal5", "goal6", "goal7", "goal8", "goal9", "goal10", "goal11", "goal12", "goal13", "goal15", "goal16", "total_affected", "total_deaths")subset_data <- disaster_data[, relevant_columns]correlation_matrix_subset <-cor(subset_data[, c("total_affected", "total_deaths")], subset_data)cor_melted <- reshape2::melt(correlation_matrix_subset)names(cor_melted) <-c("Variable2", "Variable1", "Correlation")ggplot(data = cor_melted, aes(Variable1, Variable2, fill = Correlation)) +geom_tile() +scale_fill_gradient2(low ="blue", high ="red", mid ="white",midpoint =0, limit =c(-1, 1), space ="Lab",name ="Correlation") +theme_minimal() +theme( axis.text.x =element_text(angle =45, size =8, hjust =1),axis.text.y =element_text(vjust =1, size =8, hjust =1),plot.title =element_text(margin =margin(b =20), hjust =0.5, vjust =8, lineheight =1.5) ) +coord_fixed() +labs(x ='', y ='',title ='Correlation between the climate disasters and the SDG goals in South and East Asia and North America')
>>>>>>> Stashed changes
Here you can see an extract of our correlation map between the COVID-19 and the SDG goals only during COVID-19 time. We have the same conclusions, it is still not significant, and that’s surprising.
Here you can see an extract of our correlation map between the conflicts deaths and the SDG goals in Middle East & North Africa, Sub-Saharan Africa, South Asia, Latin America & the Caribbean as it was the regions that where the most impacted.
<<<<<<< Updated upstream
Code
conflicts_filtered <- Q3.3[Q3.3$region %in%c("Middle East & North Africa", "Sub-Saharan Africa", "South Asia", "Latin America & the Caribbean"), ]relevant_columns <-c("goal1", "goal2", "goal3", "goal4", "goal5", "goal6", "goal7", "goal8", "goal9", "goal10", "goal11", "goal12", "goal13", "goal15", "goal16", "sum_deaths")subset_data <- conflicts_filtered[, relevant_columns]correlation_matrix_Conflicts_Deaths <-cor(subset_data, subset_data[, c("sum_deaths")])cor_melted <-as.data.frame(as.table(correlation_matrix_Conflicts_Deaths))names(cor_melted) <-c("Variable1", "Variable2", "Correlation")ggplot(data = cor_melted, aes(Variable1, Variable2, fill = Correlation)) +geom_tile() +scale_fill_gradient2(low ="blue", high ="red", mid ="white",midpoint =0, limit =c(-1, 1), space ="Lab",name ="Correlation") +theme_minimal() +theme( axis.text.x =element_text(angle =45, size =8, hjust =1),axis.text.y =element_text(vjust =1, size =8, hjust =1),plot.title =element_text(margin =margin(b =20), hjust =0.5, vjust =8, lineheight =2) ) +coord_fixed() +labs(x ='', y ='Deaths',title ='Correlation between Conflicts Deaths and the SDG goals')
=======
Code
conflicts_filtered <- Q3.3[Q3.3$region %in%c("Middle East & North Africa", "Sub-Saharan Africa", "South Asia", "Latin America & the Caribbean"), ]relevant_columns <-c("goal1", "goal2", "goal3", "goal4", "goal5", "goal6", "goal7", "goal8", "goal9", "goal10", "goal11", "goal12", "goal13", "goal15", "goal16", "sum_deaths")subset_data <- conflicts_filtered[, relevant_columns]correlation_matrix_Conflicts_Deaths <-cor(subset_data, subset_data[, c("sum_deaths")])cor_melted <-as.data.frame(as.table(correlation_matrix_Conflicts_Deaths))names(cor_melted) <-c("Variable1", "Variable2", "Correlation")ggplot(data = cor_melted, aes(Variable1, Variable2, fill = Correlation)) +geom_tile() +scale_fill_gradient2(low ="blue", high ="red", mid ="white",midpoint =0, limit =c(-1, 1), space ="Lab",name ="Correlation") +theme_minimal() +theme( axis.text.x =element_text(angle =45, size =8, hjust =1),axis.text.y =element_text(vjust =1, size =8, hjust =1),plot.title =element_text(margin =margin(b =20), hjust =0.5, vjust =8, lineheight =2) ) +coord_fixed() +labs(x ='', y ='Deaths',title ='Correlation between Conflicts Deaths and the SDG goals')
Here we want to analyse the correlation between conflicts affected population and the SDG goals only for the Middle East & North Africa, Sub-Saharan Africa, South Asia, Latin America & the Caribbean and Caucasus & Central Asia.
Code
conflicts_filtered <- Q3.3[Q3.3$region %in%c("Middle East & North Africa", "Sub-Saharan Africa", "South Asia", "Latin America & the Caribbean", "Caucasus & Central Asia"), ]relevant_columns <-c("goal1", "goal2", "goal3", "goal4", "goal5", "goal6", "goal7", "goal8", "goal9", "goal10", "goal11", "goal12", "goal13", "goal15", "goal16", "pop_affected")subset_data <- conflicts_filtered[, relevant_columns]correlation_matrix_Conflicts_Pop_Aff <-cor(subset_data, subset_data[, c("pop_affected")])cor_melted <-as.data.frame(as.table(correlation_matrix_Conflicts_Pop_Aff))names(cor_melted) <-c("Variable1", "Variable2", "Correlation")ggplot(data = cor_melted, aes(Variable1, Variable2, fill = Correlation)) +geom_tile() +scale_fill_gradient2(low ="blue", high ="red", mid ="white",midpoint =0, limit =c(-1, 1), space ="Lab",name ="Correlation") +theme_minimal() +theme( axis.text.x =element_text(angle =45, size =8, hjust =1),axis.text.y =element_text(vjust =1, size =8, hjust =1),plot.title =element_text(margin =margin(b =20), hjust =0.5, vjust =8, lineheight =2) ) +coord_fixed() +labs(x ='', y ='Affected Population',title ='Correlation between Conflicts Affected Population and the SDG goals')
>>>>>>> Stashed changes
<<<<<<< Updated upstream
Here we want to analyse the correlation between conflicts affected population and the SDG goals only for the Middle East & North Africa, Sub-Saharan Africa, South Asia, Latin America & the Caribbean and Caucasus & Central Asia.
Code
conflicts_filtered <- Q3.3[Q3.3$region %in%c("Middle East & North Africa", "Sub-Saharan Africa", "South Asia", "Latin America & the Caribbean", "Caucasus & Central Asia"), ]relevant_columns <-c("goal1", "goal2", "goal3", "goal4", "goal5", "goal6", "goal7", "goal8", "goal9", "goal10", "goal11", "goal12", "goal13", "goal15", "goal16", "pop_affected")subset_data <- conflicts_filtered[, relevant_columns]correlation_matrix_Conflicts_Pop_Aff <-cor(subset_data, subset_data[, c("pop_affected")])cor_melted <-as.data.frame(as.table(correlation_matrix_Conflicts_Pop_Aff))names(cor_melted) <-c("Variable1", "Variable2", "Correlation")ggplot(data = cor_melted, aes(Variable1, Variable2, fill = Correlation)) +geom_tile() +scale_fill_gradient2(low ="blue", high ="red", mid ="white",midpoint =0, limit =c(-1, 1), space ="Lab",name ="Correlation") +theme_minimal() +theme( axis.text.x =element_text(angle =45, size =8, hjust =1),axis.text.y =element_text(vjust =1, size =8, hjust =1),plot.title =element_text(margin =margin(b =20), hjust =0.5, vjust =8, lineheight =2) ) +coord_fixed() +labs(x ='', y ='Affected Population',title ='Correlation between Conflicts Affected Population and the SDG goals')
=======
Here we want to analyse the correlation between the regions having a maxintensity in conflicts and the SDG goals only for Caucasus & Central Asia, Eastern Europe, Middle east & north Africa, Sub-Saharan Africa, North America, South Asia, East Asia, Latin America & the Caribbean.
Code
conflicts_filtered <- Q3.3[Q3.3$region %in%c("Middle East & North Africa", "Sub-Saharan Africa", "South Asia", "Latin America & the Caribbean", "Caucasus & Central Asia", "Eastern Europe", "North America", "East Asia"), ]relevant_columns <-c("goal1", "goal2", "goal3", "goal4", "goal5", "goal6", "goal7", "goal8", "goal9", "goal10", "goal11", "goal12", "goal13", "goal15", "goal16", "maxintensity")subset_data <- conflicts_filtered[, relevant_columns]correlation_matrix_Conflicts_Maxintensity <-cor(subset_data, subset_data[, c("maxintensity")])cor_melted <-as.data.frame(as.table(correlation_matrix_Conflicts_Maxintensity))names(cor_melted) <-c("Variable1", "Variable2", "Correlation")ggplot(data = cor_melted, aes(Variable1, Variable2, fill = Correlation)) +geom_tile() +scale_fill_gradient2(low ="blue", high ="red", mid ="white",midpoint =0, limit =c(-1, 1), space ="Lab",name ="Correlation") +theme_minimal() +theme( axis.text.x =element_text(angle =45, size =8, hjust =1),axis.text.y =element_text(vjust =1, size =8, hjust =1),plot.title =element_text(margin =margin(b =20), hjust =0.5, vjust =8, lineheight =2) ) +coord_fixed() +labs(x ='', y ='Maxintensity',title ='Correlation between Maxintensity in Conflicts and the SDG goals')
>>>>>>> Stashed changes
Here we want to analyse the correlation between the regions having a maxintensity in conflicts and the SDG goals only for Caucasus & Central Asia, Eastern Europe, Middle east & north Africa, Sub-Saharan Africa, North America, South Asia, East Asia, Latin America & the Caribbean.
Code
conflicts_filtered <- Q3.3[Q3.3$region %in%c("Middle East & North Africa", "Sub-Saharan Africa", "South Asia", "Latin America & the Caribbean", "Caucasus & Central Asia", "Eastern Europe", "North America", "East Asia"), ]relevant_columns <-c("goal1", "goal2", "goal3", "goal4", "goal5", "goal6", "goal7", "goal8", "goal9", "goal10", "goal11", "goal12", "goal13", "goal15", "goal16", "maxintensity")subset_data <- conflicts_filtered[, relevant_columns]correlation_matrix_Conflicts_Maxintensity <-cor(subset_data, subset_data[, c("maxintensity")])cor_melted <-as.data.frame(as.table(correlation_matrix_Conflicts_Maxintensity))names(cor_melted) <-c("Variable1", "Variable2", "Correlation")ggplot(data = cor_melted, aes(Variable1, Variable2, fill = Correlation)) +geom_tile() +scale_fill_gradient2(low ="blue", high ="red", mid ="white",midpoint =0, limit =c(-1, 1), space ="Lab",name ="Correlation") +theme_minimal() +theme( axis.text.x =element_text(angle =45, size =8, hjust =1),axis.text.y =element_text(vjust =1, size =8, hjust =1),plot.title =element_text(margin =margin(b =20), hjust =0.5, vjust =8, lineheight =2) ) +coord_fixed() +labs(x ='', y ='Maxintensity',title ='Correlation between Maxintensity in Conflicts and the SDG goals')
After looking at almost the same results, we asked ourselves if the fact that we do not see any correlations is because the consequences of this disasters arrive later on, so we decided to remake the same correlations with 1 year gap.
3.5.1 Correlations for each event with one year gap
Here you can see for example our correlation map between the climate disasters and the SDG goals in South and East Asia and North America with one year gap.
<<<<<<< Updated upstream
Code
disaster_data <- Q3.1[Q3.1$region %in%c("South Asia", "East Asia", " North America"), ]relevant_columns <-c("goal1", "goal2", "goal3", "goal4", "goal5", "goal6", "goal7", "goal8", "goal9", "goal10", "goal11", "goal12", "goal13", "goal15", "goal16", "total_affected", "total_deaths")subset_data <- disaster_data[, relevant_columns]lagged_subset_data <- subset_data %>%mutate(lagged_total_affected =lag(total_affected, default =NA),lagged_total_deaths =lag(total_deaths, default =NA) )correlation_matrix_lagged <-cor(lagged_subset_data[, c("lagged_total_affected", "lagged_total_deaths")], subset_data)cor_melted_lagged <- reshape2::melt(correlation_matrix_lagged)names(cor_melted_lagged) <-c("Variable2", "Variable1", "Correlation")ggplot(data = cor_melted_lagged, aes(Variable1, Variable2, fill = Correlation)) +geom_tile() +scale_fill_gradient2(low ="blue", high ="red", mid ="white",midpoint =0, limit =c(-1, 1), space ="Lab",name ="Correlation") +theme_minimal() +theme( axis.text.x =element_text(angle =45, size =8, hjust =1),axis.text.y =element_text(vjust =1, size =8, hjust =2),plot.title =element_text(margin =margin(b =20), hjust =0.5, vjust =6, lineheight =1.5),legend.title =element_text(size =8) ) +coord_fixed() +labs(x ='', y ='',title ='Correlation between the climate disasters and the SDG goals in South and East Asia with 1 year gap')+theme(plot.title =element_text(size =8, vjust =12))
=======
Code
disaster_data <- Q3.1[Q3.1$region %in%c("South Asia", "East Asia", " North America"), ]relevant_columns <-c("goal1", "goal2", "goal3", "goal4", "goal5", "goal6", "goal7", "goal8", "goal9", "goal10", "goal11", "goal12", "goal13", "goal15", "goal16", "total_affected", "total_deaths")subset_data <- disaster_data[, relevant_columns]lagged_subset_data <- subset_data %>%mutate(lagged_total_affected =lag(total_affected, default =NA),lagged_total_deaths =lag(total_deaths, default =NA) )correlation_matrix_lagged <-cor(lagged_subset_data[, c("lagged_total_affected", "lagged_total_deaths")], subset_data)cor_melted_lagged <- reshape2::melt(correlation_matrix_lagged)names(cor_melted_lagged) <-c("Variable2", "Variable1", "Correlation")ggplot(data = cor_melted_lagged, aes(Variable1, Variable2, fill = Correlation)) +geom_tile() +scale_fill_gradient2(low ="blue", high ="red", mid ="white",midpoint =0, limit =c(-1, 1), space ="Lab",name ="Correlation") +theme_minimal() +theme( axis.text.x =element_text(angle =45, size =8, hjust =1),axis.text.y =element_text(vjust =1, size =8, hjust =2),plot.title =element_text(margin =margin(b =20), hjust =0.5, vjust =6, lineheight =1.5),legend.title =element_text(size =8) ) +coord_fixed() +labs(x ='', y ='',title ='Correlation between the climate disasters and the SDG goals in South and East Asia with 1 year gap')+theme(plot.title =element_text(size =8, vjust =12))
>>>>>>> Stashed changes
Even with a year gap it doesn’t seem that climate disaster with such consequences as the population that gets affected and dies has an impact on the SDG scores as we would have though. But we are still a little bit optimistic and though why not look at the correlations with a gap year over the years.
3.5.2 Interactive map of the correlation between the different events and the SDG goals with 1 year gap.
Here you can see an interactive map of the correlation between the climate disasters and the SDG goals in South and East Asia with 1 year gap. To better understand the results, if we select a specific year (e.g., 2020) in the app, the analysis will show correlations between the SDG scores for the selected year (e.g., 2020) and the disaster-related variables (total_affected and total_deaths) from the previous year (e.g., 2019).
<<<<<<< Updated upstream
=======
Shiny applications not supported in static R Markdown documents
here you can see an interactive map of the correlation between COVID-19 and the SDG goals with 1 year gap. And strangely, instead of having a negative correlation, we expected that the more cases and deaths happened because of COVID-19, the scores of the SDG would be negatively affected,but with the gap year we can see that the scores of the Goal3, Goal6, Goal9 and Goal16 are quite positively impacted by the COVID-19.
Code
library(shiny)library(plotly)Q3.2<- Q3.2%>%arrange(code,year)%>%group_by(code)Q3.2<-read.csv(here("scripts", "data", "data_question3_2.csv"))covid_filtered <- Q3.2relevant_columns <-c("goal1", "goal2", "goal3", "goal4", "goal5", "goal6", "goal7", "goal8", "goal9", "goal10", "goal11", "goal12", "goal13", "goal15", "goal16", "stringency", "cases_per_million", "deaths_per_million")subset_data <- covid_filtered[, relevant_columns]correlation_matrix_Covid <-cor(subset_data, subset_data[, c("stringency", "cases_per_million", "deaths_per_million")])cor_melted <-as.data.frame(as.table(correlation_matrix_Covid))names(cor_melted) <-c("Variable1", "Variable2", "Correlation")ui <-fluidPage(titlePanel("Interactive Correlation Heatmap between COVID and the SDG goal with one year gap"),plotlyOutput("heatmap"),sliderInput("year", "Select Year", min =2020, max =2022, value =2020, step =1),actionButton("stopButton", "Stop application"))server <-function(input, output, session) { selected_covid_data <-reactive({ filtered_data <- covid_filtered[covid_filtered$year == input$year, ] subset_data <- filtered_data[, relevant_columns]return(subset_data) }) output$heatmap <-renderPlotly({ correlation_matrix_Covid <-cor(selected_covid_data(), selected_covid_data()[, c("stringency", "cases_per_million", "deaths_per_million")]) cor_melted <-as.data.frame(as.table(correlation_matrix_Covid))names(cor_melted) <-c("Variable1", "Variable2", "Correlation") p <-plot_ly(data = cor_melted, x =~Variable1, y =~Variable2, z =~Correlation,type ="heatmap", colorscale =list(c(0, "blue"), c(0.5, "white"), c(1, "red")),zmin =-1, zmax =1) p <- p %>%layout(title ="",xaxis =list(title =""),yaxis =list(title =""),coloraxis =list(colorbar =list(title ="Correlation",tickvals =c(-1, 0, 1), ticktext =c("-1", "0", "1"),len =5, thickness =20, x =0,xanchor ="left", ticks ="outside" ) ) )return(p) })observeEvent(input$stopButton, {stopApp() })}shinyApp(ui = ui, server = server)
>>>>>>> Stashed changes
Shiny applications not supported in static R Markdown documents
here you can see an interactive map of the correlation between COVID-19 and the SDG goals with 1 year gap. And strangely, instead of having a negative correlation, we expected that the more cases and deaths happened because of COVID-19, the scores of the SDG would be negatively affected,but with the gap year we can see that the scores of the Goal3, Goal6, Goal9 and Goal16 are quite positively impacted by the COVID-19.
Code
<<<<<<< Updated upstream
library(shiny)library(plotly)Q3.2<- Q3.2%>%arrange(code,year)%>%group_by(code)Q3.2<-read.csv(here("scripts", "data", "data_question3_2.csv"))covid_filtered <- Q3.2relevant_columns <-c("goal1", "goal2", "goal3", "goal4", "goal5", "goal6", "goal7", "goal8", "goal9", "goal10", "goal11", "goal12", "goal13", "goal15", "goal16", "stringency", "cases_per_million", "deaths_per_million")subset_data <- covid_filtered[, relevant_columns]correlation_matrix_Covid <-cor(subset_data, subset_data[, c("stringency", "cases_per_million", "deaths_per_million")])cor_melted <-as.data.frame(as.table(correlation_matrix_Covid))names(cor_melted) <-c("Variable1", "Variable2", "Correlation")ui <-fluidPage(titlePanel("Interactive Correlation Heatmap between COVID and the SDG goal with one year gap"),plotlyOutput("heatmap"),sliderInput("year", "Select Year", min =2020, max =2022, value =2020, step =1),actionButton("stopButton", "Stop application"))server <-function(input, output, session) { selected_covid_data <-reactive({ filtered_data <- covid_filtered[covid_filtered$year == input$year, ] subset_data <- filtered_data[, relevant_columns]return(subset_data) }) output$heatmap <-renderPlotly({ correlation_matrix_Covid <-cor(selected_covid_data(), selected_covid_data()[, c("stringency", "cases_per_million", "deaths_per_million")]) cor_melted <-as.data.frame(as.table(correlation_matrix_Covid))names(cor_melted) <-c("Variable1", "Variable2", "Correlation") p <-plot_ly(data = cor_melted, x =~Variable1, y =~Variable2, z =~Correlation,type ="heatmap", colorscale =list(c(0, "blue"), c(0.5, "white"), c(1, "red")),zmin =-1, zmax =1) p <- p %>%layout(title ="",xaxis =list(title ="", tickfont =list(size =16)),yaxis =list(title ="", tickfont =list(size =16)),coloraxis =list(colorbar =list(title ="Correlation",tickvals =c(-1, 0, 1), ticktext =c("-1", "0", "1"),len =5, thickness =20, x =0,xanchor ="left", ticks ="outside" ) ) )return(p) })observeEvent(input$stopButton, {stopApp() })}shinyApp(ui = ui, server = server)
=======
library(shiny)library(plotly)Q3.3<- Q3.3%>%arrange(code,year)%>%group_by(code)conflicts_filtered <- Q3.3[Q3.3$region %in%c("Middle East & North Africa", "Sub-Saharan Africa", "South Asia", "Latin America & the Caribbean"), ]relevant_columns <-c("goal1", "goal2", "goal3", "goal4", "goal5", "goal6", "goal7", "goal8", "goal9", "goal10", "goal11", "goal12", "goal13", "goal15", "goal16", "sum_deaths")subset_data <- conflicts_filtered[, relevant_columns]correlation_matrix_Conflicts_Deaths <-cor(subset_data, subset_data[, c("sum_deaths")])cor_melted <-as.data.frame(as.table(correlation_matrix_Conflicts_Deaths))names(cor_melted) <-c("Variable1", "Variable2", "Correlation")ui <-fluidPage(titlePanel("Interactive Correlation Heatmap between Conflicts Deaths in selected regions and the SDG goal with one year gap"),plotlyOutput("heatmap"),sliderInput("year", "Select Year", min =2000, max =2016, value =2005, step =1),actionButton("stopButton", "Stop application"))server <-function(input, output, session) { selected_conflicts_data <-reactive({ filtered_data <- conflicts_filtered[conflicts_filtered$year == input$year, ] subset_data <- filtered_data[, relevant_columns]return(subset_data) }) output$heatmap <-renderPlotly({ correlation_matrix_Conflicts_Deaths <-cor(selected_conflicts_data(), selected_conflicts_data()[, c("sum_deaths")]) cor_melted <-as.data.frame(as.table(correlation_matrix_Conflicts_Deaths))names(cor_melted) <-c("Variable1", "Variable2", "Correlation") p <-plot_ly(data = cor_melted, x =~Variable1, y =~Variable2, z =~Correlation,type ="heatmap", colorscale =list(c(-1, "blue"), c(0, "white"), c(1, "red")),zmin =-1, zmax =1) p <- p %>%layout(title ="",xaxis =list(title =""),yaxis =list(title =""),coloraxis =list(colorbar =list(title ="Correlation",tickvals =c(-1, 0, 1), ticktext =c("-1", "0", "1"), len =5, thickness =20, x =0, xanchor ="left", ticks ="outside" ) ) )return(p) })observeEvent(input$stopButton, {stopApp() })}shinyApp(ui = ui, server = server)
>>>>>>> Stashed changes
Shiny applications not supported in static R Markdown documents
Finally, here you can see an interactive map of the correlation between respectively for the 3 different variables of the Conflict and the SDG goals with 1 year gap.
Code
<<<<<<< Updated upstream
library(shiny)library(plotly)Q3.3<- Q3.3%>%arrange(code,year)%>%group_by(code)conflicts_filtered <- Q3.3[Q3.3$region %in%c("Middle East & North Africa", "Sub-Saharan Africa", "South Asia", "Latin America & the Caribbean"), ]relevant_columns <-c("goal1", "goal2", "goal3", "goal4", "goal5", "goal6", "goal7", "goal8", "goal9", "goal10", "goal11", "goal12", "goal13", "goal15", "goal16", "sum_deaths")subset_data <- conflicts_filtered[, relevant_columns]correlation_matrix_Conflicts_Deaths <-cor(subset_data, subset_data[, c("sum_deaths")])cor_melted <-as.data.frame(as.table(correlation_matrix_Conflicts_Deaths))names(cor_melted) <-c("Variable1", "Variable2", "Correlation")ui <-fluidPage(titlePanel("Interactive Correlation Heatmap between Conflicts Deaths in selected regions and the SDG goal with one year gap"),plotlyOutput("heatmap"),sliderInput("year", "Select Year", min =2000, max =2016, value =2005, step =1),actionButton("stopButton", "Stop application"))server <-function(input, output, session) { selected_conflicts_data <-reactive({ filtered_data <- conflicts_filtered[conflicts_filtered$year == input$year, ] subset_data <- filtered_data[, relevant_columns]return(subset_data) }) output$heatmap <-renderPlotly({ correlation_matrix_Conflicts_Deaths <-cor(selected_conflicts_data(), selected_conflicts_data()[, c("sum_deaths")]) cor_melted <-as.data.frame(as.table(correlation_matrix_Conflicts_Deaths))names(cor_melted) <-c("Variable1", "Variable2", "Correlation") p <-plot_ly(data = cor_melted, x =~Variable1, y =~Variable2, z =~Correlation,type ="heatmap", colorscale =list(c(-1, "blue"), c(0, "white"), c(1, "red")),zmin =-1, zmax =1) p <- p %>%layout(title ="",xaxis =list(title =""),yaxis =list(title =""),coloraxis =list(colorbar =list(title ="Correlation",tickvals =c(-1, 0, 1), ticktext =c("-1", "0", "1"), len =5, thickness =20, x =0, xanchor ="left", ticks ="outside" ) ) )return(p) })observeEvent(input$stopButton, {stopApp() })}shinyApp(ui = ui, server = server)
=======
library(shiny)library(plotly)Q3.3<- Q3.3%>%arrange(code,year)%>%group_by(code)conflicts_filtered <- Q3.3[Q3.3$region %in%c("Middle East & North Africa", "Sub-Saharan Africa", "South Asia", "Latin America & the Caribbean", "Caucasus & Central Asia"), ]relevant_columns <-c("goal1", "goal2", "goal3", "goal4", "goal5", "goal6", "goal7", "goal8", "goal9", "goal10", "goal11", "goal12", "goal13", "goal15", "goal16", "pop_affected")subset_data <- conflicts_filtered[, relevant_columns]correlation_matrix_Conflicts_Pop_Aff <-cor(subset_data, subset_data[, c("pop_affected")])cor_melted <-as.data.frame(as.table(correlation_matrix_Conflicts_Pop_Aff))names(cor_melted) <-c("Variable1", "Variable2", "Correlation")ui <-fluidPage(titlePanel("Interactive Correlation Heatmap between population affected in conflicts in selected regions and the SDG goal with one year gap"),plotlyOutput("heatmap"),sliderInput("year", "Select Year", min =2000, max =2016, value =2005, step =1),actionButton("stopButton", "Stop application"))server <-function(input, output, session) { selected_conflicts_data <-reactive({ filtered_data <- conflicts_filtered[conflicts_filtered$year == input$year, ] subset_data <- filtered_data[, relevant_columns]return(subset_data) }) output$heatmap <-renderPlotly({ correlation_matrix_Conflicts_Pop_Aff <-cor(selected_conflicts_data(), selected_conflicts_data()[, c("pop_affected")]) cor_melted <-as.data.frame(as.table(correlation_matrix_Conflicts_Pop_Aff))names(cor_melted) <-c("Variable1", "Variable2", "Correlation") p <-plot_ly(data = cor_melted, x =~Variable1, y =~Variable2, z =~Correlation,type ="heatmap", colorscale =list(c(-1, "blue"), c(0, "white"), c(1, "red")),zmin =-1, zmax =1) p <- p %>%layout(title ="",xaxis =list(title =""),yaxis =list(title =""),coloraxis =list(colorbar =list(title ="Correlation",tickvals =c(-1, 0, 1), ticktext =c("-1", "0", "1"), len =5, thickness =20, x =0, xanchor ="left", ticks ="outside" ) ) )return(p) })observeEvent(input$stopButton, {stopApp() })}shinyApp(ui = ui, server = server)
>>>>>>> Stashed changes
Shiny applications not supported in static R Markdown documents
Here’s the interactive map of the correlation between the affected population in conflicts and the SDG goals with 1 year gap.
Code
<<<<<<< Updated upstream
library(shiny)library(plotly)Q3.3<- Q3.3%>%arrange(code,year)%>%group_by(code)conflicts_filtered <- Q3.3[Q3.3$region %in%c("Middle East & North Africa", "Sub-Saharan Africa", "South Asia", "Latin America & the Caribbean", "Caucasus & Central Asia"), ]relevant_columns <-c("goal1", "goal2", "goal3", "goal4", "goal5", "goal6", "goal7", "goal8", "goal9", "goal10", "goal11", "goal12", "goal13", "goal15", "goal16", "pop_affected")subset_data <- conflicts_filtered[, relevant_columns]correlation_matrix_Conflicts_Pop_Aff <-cor(subset_data, subset_data[, c("pop_affected")])cor_melted <-as.data.frame(as.table(correlation_matrix_Conflicts_Pop_Aff))names(cor_melted) <-c("Variable1", "Variable2", "Correlation")ui <-fluidPage(titlePanel("Interactive Correlation Heatmap between population affected in conflicts in selected regions and the SDG goal with one year gap"),plotlyOutput("heatmap"),sliderInput("year", "Select Year", min =2000, max =2016, value =2005, step =1),actionButton("stopButton", "Stop application"))server <-function(input, output, session) { selected_conflicts_data <-reactive({ filtered_data <- conflicts_filtered[conflicts_filtered$year == input$year, ] subset_data <- filtered_data[, relevant_columns]return(subset_data) }) output$heatmap <-renderPlotly({ correlation_matrix_Conflicts_Pop_Aff <-cor(selected_conflicts_data(), selected_conflicts_data()[, c("pop_affected")]) cor_melted <-as.data.frame(as.table(correlation_matrix_Conflicts_Pop_Aff))names(cor_melted) <-c("Variable1", "Variable2", "Correlation") p <-plot_ly(data = cor_melted, x =~Variable1, y =~Variable2, z =~Correlation,type ="heatmap", colorscale =list(c(-1, "blue"), c(0, "white"), c(1, "red")),zmin =-1, zmax =1) p <- p %>%layout(title ="",xaxis =list(title =""),yaxis =list(title =""),coloraxis =list(colorbar =list(title ="Correlation",tickvals =c(-1, 0, 1), ticktext =c("-1", "0", "1"), len =5, thickness =20, x =0, xanchor ="left", ticks ="outside" ) ) )return(p) })observeEvent(input$stopButton, {stopApp() })}shinyApp(ui = ui, server = server)
=======
library(shiny)library(plotly)Q3.3<- Q3.3%>%arrange(code,year)%>%group_by(code)conflicts_filtered <- Q3.3[Q3.3$region %in%c("Middle East & North Africa", "Sub-Saharan Africa", "South Asia", "Latin America & the Caribbean", "Caucasus & Central Asia", "Eastern Europe", "North America", "East Asia"), ]relevant_columns <-c("goal1", "goal2", "goal3", "goal4", "goal5", "goal6", "goal7", "goal8", "goal9", "goal10", "goal11", "goal12", "goal13", "goal15", "goal16", "maxintensity")subset_data <- conflicts_filtered[, relevant_columns]correlation_matrix_Conflicts_Max <-cor(subset_data, subset_data[, c("maxintensity")])cor_melted <-as.data.frame(as.table(correlation_matrix_Conflicts_Max))names(cor_melted) <-c("Variable1", "Variable2", "Correlation")ui <-fluidPage(titlePanel("Interactive Correlation Heatmap between population affected in conflicts in selected regions and the SDG goal with one year gap"),plotlyOutput("heatmap"),sliderInput("year", "Select Year", min =2000, max =2016, value =2005, step =1),actionButton("stopButton", "Stop application"))server <-function(input, output, session) { selected_conflicts_data <-reactive({ filtered_data <- conflicts_filtered[conflicts_filtered$year == input$year, ] subset_data <- filtered_data[, relevant_columns]return(subset_data) }) output$heatmap <-renderPlotly({ correlation_matrix_Conflicts_Max <-cor(selected_conflicts_data(), selected_conflicts_data()[, c("maxintensity")]) cor_melted <-as.data.frame(as.table(correlation_matrix_Conflicts_Max))names(cor_melted) <-c("Variable1", "Variable2", "Correlation") p <-plot_ly(data = cor_melted, x =~Variable1, y =~Variable2, z =~Correlation,type ="heatmap", colorscale =list(c(-1, "blue"), c(0, "white"), c(1, "red")),zmin =-1, zmax =1) p <- p %>%layout(title ="",xaxis =list(title =""),yaxis =list(title =""),coloraxis =list(colorbar =list(title ="Correlation",tickvals =c(-1, 0, 1), ticktext =c("-1", "0", "1"), len =5, thickness =20, x =0, xanchor ="left", ticks ="outside" ) ) )return(p) })observeEvent(input$stopButton, {stopApp() })}shinyApp(ui = ui, server = server)
>>>>>>> Stashed changes
Shiny applications not supported in static R Markdown documents
Here’s the interactive map of the correlation between the Maxintensity in conflicts and the SDG goals with 1 year gap.
Code
library(shiny)library(plotly)Q3.3<- Q3.3%>%arrange(code,year)%>%group_by(code)conflicts_filtered <- Q3.3[Q3.3$region %in%c("Middle East & North Africa", "Sub-Saharan Africa", "South Asia", "Latin America & the Caribbean", "Caucasus & Central Asia", "Eastern Europe", "North America", "East Asia"), ]relevant_columns <-c("goal1", "goal2", "goal3", "goal4", "goal5", "goal6", "goal7", "goal8", "goal9", "goal10", "goal11", "goal12", "goal13", "goal15", "goal16", "maxintensity")subset_data <- conflicts_filtered[, relevant_columns]correlation_matrix_Conflicts_Max <-cor(subset_data, subset_data[, c("maxintensity")])cor_melted <-as.data.frame(as.table(correlation_matrix_Conflicts_Max))names(cor_melted) <-c("Variable1", "Variable2", "Correlation")ui <-fluidPage(titlePanel("Interactive Correlation Heatmap between population affected in conflicts in selected regions and the SDG goal with one year gap"),plotlyOutput("heatmap"),sliderInput("year", "Select Year", min =2000, max =2016, value =2005, step =1),actionButton("stopButton", "Stop application"))server <-function(input, output, session) { selected_conflicts_data <-reactive({ filtered_data <- conflicts_filtered[conflicts_filtered$year == input$year, ] subset_data <- filtered_data[, relevant_columns]return(subset_data) }) output$heatmap <-renderPlotly({ correlation_matrix_Conflicts_Max <-cor(selected_conflicts_data(), selected_conflicts_data()[, c("maxintensity")]) cor_melted <-as.data.frame(as.table(correlation_matrix_Conflicts_Max))names(cor_melted) <-c("Variable1", "Variable2", "Correlation") p <-plot_ly(data = cor_melted, x =~Variable1, y =~Variable2, z =~Correlation,type ="heatmap", colorscale =list(c(-1, "blue"), c(0, "white"), c(1, "red")),zmin =-1, zmax =1) p <- p %>%layout(title ="",xaxis =list(title =""),yaxis =list(title =""),coloraxis =list(colorbar =list(title ="Correlation",tickvals =c(-1, 0, 1), ticktext =c("-1", "0", "1"), len =5, thickness =20, x =0, xanchor ="left", ticks ="outside" ) ) )return(p) })observeEvent(input$stopButton, {stopApp() })}shinyApp(ui = ui, server = server)
Shiny applications not supported in static R Markdown documents
The results seems logic because if the SDG scores continue to go higher and the conflicts remains the same or finishes we get a negative correlation
Our last idea is to see the regressions between the SDG scores and the variables of each event that we thought interesting.
3.5.3 Regressions between the SDG scores and the events variables.
Let’s see the regressions for each score depending of each variable in the disasters dataset (total_affected and total_deaths)
Shiny applications not supported in static R Markdown documents
Most relationships between the goals and the variables (‘total_affected’ and ‘total_deaths’) are not statistically significant (indicated by p-values > 0.05). More specifically, in several models, the coefficients for ‘total_affected’ and ‘total_deaths’ are small, indicating weak or negligible relationships with the respective goals. Some models have marginally significant p-values (close to 0.05) but still lack statistical significance. Goals 7, 8, 10, 13, 14, and 15 exhibit statistically significant relationships with ‘total_affected,’ indicating small to moderate positive relationships. Goals 7 and 8 also show statistically significant relationships with ‘total_deaths,’ indicating moderate negative relationships.
These findings suggest that, in most cases, the relationships between the specified goals and COVID-19 variables (total affected and total deaths) are not statistically significant. However, some goals do indicate small to moderate associations with these variables.
Let’s see the regressions for each score depending of each variable in the COVID-19 dataset (stringency, cases_per_million and deaths_per_million).
Shiny applications not supported in static R Markdown documents
For all objectives (from objective 1 to objective 16), the predictor variables (stringency, number of cases per million and number of deaths per million) show statistically significant relationships. However, when assessed individually, these predictors explain only a marginal fraction of the variance of the respective objectives, with explanatory percentages ranging from around 0.141% to 6.99%. Adjusted R-squared values consistently indicate limited explanatory power for these relationships, implying the influence of other factors not accounted for in the variations observed for each objective. Relying solely on rigor, cases per million and deaths per million results in modest predictive capabilities for each objective.
In summary, the statistical significance of stringency, cases per million and deaths per million in relation to each objective is clear. However, these predictive variables individually fail to explain the variations observed, highlighting the need to explore additional variables or unexplored factors in order to significantly improve predictive ability for each respective objective.
Now, let’s see the regressions for each SDG score depending of each variable in the Conflicts dataset (pop_affected and sum_deaths).
conflicts_filtered <- Q3.3[Q3.3$region %in%c("Middle East & North Africa", "Sub-Saharan Africa", "South Asia", "Latin America & the Caribbean", "Caucasus and Central Asia"), ]relevant_columns <-c("goal1", "goal2", "goal3", "goal4", "goal5", "goal6", "goal7", "goal8", "goal9", "goal10", "goal11", "goal12", "goal13", "goal15", "goal16", "pop_affected", "sum_deaths", "maxintensity")subset_data <- conflicts_filtered[, relevant_columns]goal_columns <-c("goal1", "goal2", "goal3", "goal4", "goal5", "goal6", "goal7", "goal8", "goal9", "goal10", "goal11", "goal12", "goal13", "goal15", "goal16")ui <-fluidPage(titlePanel("SDG - Conflicts Regression Analysis"),sidebarLayout(sidebarPanel(selectInput("sdg", "Select SDG Goal:",choices = goal_columns,selected = goal_columns[1]),actionButton("stopButton", "Stop Rendering"), # Adding a Stop buttonwidth =3 ),mainPanel(width =9,plotOutput("regression_plot_affected"),plotOutput("regression_plot_deaths"),plotOutput("regression_plot_maxintensity") ) ))server <-function(input, output, session) {observeEvent(input$stopButton, { output$regression_plot_affected <-NULL output$regression_plot_deaths <-NULL output$regression_plot_maxintensity <-NULL }) generate_regression_plot <-function(selected_goal) { formula_pop_affected <-as.formula(paste(selected_goal, "~ pop_affected")) formula_sum_deaths <-as.formula(paste(selected_goal, "~ sum_deaths")) formula_maxintensity <-as.formula(paste(selected_goal, "~maxintensity")) lm_pop_affected <-lm(formula_pop_affected, data = subset_data) lm_sum_deaths <-lm(formula_sum_deaths, data = subset_data) lm_maxintensity <-lm(formula_maxintensity, data = subset_data) plot_pop_affected <-ggplot(subset_data, aes(x = pop_affected, y =!!as.name(selected_goal))) +geom_point() +geom_smooth(method ="lm", se =FALSE) +labs(title =paste("Regression plot for", selected_goal, "vs Population Affected"),x ="Population Affected", y = selected_goal) +scale_x_continuous(labels =comma_format()) plot_sum_deaths <-ggplot(subset_data, aes(x = sum_deaths, y =!!as.name(selected_goal))) +geom_point() +geom_smooth(method ="lm", se =FALSE) +labs(title =paste("Regression plot for", selected_goal, "vs Sum of Deaths"),x ="Sum of Deaths", y = selected_goal) +scale_x_continuous(labels =comma_format()) plot_maxintensity <-ggplot(subset_data, aes(x = maxintensity, y =!!as.name(selected_goal))) +geom_point() +geom_smooth(method ="lm", se =FALSE) +labs(title =paste("Regression plot for", selected_goal, "vs Maxintensity"),x ="Maxintensity", y = selected_goal) +scale_x_continuous(labels =comma_format()) list(plot_pop_affected, plot_sum_deaths, plot_maxintensity) } output$regression_plot_affected <-renderPlot({req(input$sdg)isolate({ selected_goal <- input$sdggenerate_regression_plot(selected_goal)[[1]] }) }) output$regression_plot_deaths <-renderPlot({req(input$sdg)isolate({ selected_goal <- input$sdggenerate_regression_plot(selected_goal)[[2]] }) }) output$regression_plot_maxintensity <-renderPlot({req(input$sdg)isolate({ selected_goal <- input$sdggenerate_regression_plot(selected_goal)[[3]] }) })}shinyApp(ui, server)
>>>>>>> Stashed changes
Shiny applications not supported in static R Markdown documents
All three predictors exhibited statistically significant relationships with the respective goals across the board. ‘Maxintensity’ generally demonstrated a relatively stronger association compared to ‘Population Affected’ and ‘Deaths’ in most analyses. But collectively, these predictors explained only a small to moderate portion of the variability observed in the different goals (adjusted R-squared ranging from approximately 1% to 9.48%). This suggests that there are other unaccounted factors not included in the analysis that significantly influence the outcomes of these goals. To conclude, while ‘Population Affected,’ ‘Deaths,’ and ‘Maxintensity’ consistently showed significant associations with the various goals analyzed, their combined effect explained only a fraction of the variance observed in these goals. Therefore, there are likely additional crucial factors beyond these predictors that play substantial roles in influencing the outcomes of the respective goals.
4 Analysis
4.1 Answers to the research questions
4.1.1 Influence of the factors over the Sustainable Development Goals
In order to answer the first question of our work, let’s start by zooming on the correlation matrix heatmap made in our EDA part. Here are the correlations between the SDG goals and all the other variables except the SDG goals.
<<<<<<< Updated upstream
Code
### Correlation Matrix Heatmap SDG/Other variables ####computing pvals of our interested variablescorr_matrix <-cor(data_question1[7:40], method ="spearman", use ="everything")p_matrix2 <-matrix(nrow =ncol(data_question1[7:40]), ncol =ncol(data_question1[7:40]))for (i in1:ncol(data_question1[7:40])) {for (j in1:ncol(data_question1[7:40])) { test_result <-cor.test(data_question1[7:40][, i], data_question1[7:40][, j]) p_matrix2[i, j] <- test_result$p.value }}corr_matrix[which(p_matrix2 >0.05)] <-NA#only keeping significant pval alpha = 0.05melted_corr_matrix_GVar <-melt(corr_matrix[19:34,1:18])ggplot(melted_corr_matrix_GVar, aes(Var1, Var2, fill = value)) +geom_tile() +geom_text(aes(label =ifelse(!is.na(value) &abs(value) >0.75, sprintf("%.2f", value), '')),color ="black", size =2) +scale_fill_gradient2(low ="blue", high ="red", mid ="white",midpoint =0, limit =c(-1, 1), space ="Lab",name ="Spearman\nCorrelation") +theme_minimal() +theme(axis.text.x =element_text(angle =45, hjust =1),axis.text.y =element_text(angle =45, hjust =1)) +labs(x ='Goals', y ='Goals',title ='Correlations Heatmap between goals and our other variables')
=======
Code
### Correlation Matrix Heatmap SDG/Other variables ####computing pvals of our interested variablescorr_matrix <-cor(data_question1[7:40], method ="spearman", use ="everything")p_matrix2 <-matrix(nrow =ncol(data_question1[7:40]), ncol =ncol(data_question1[7:40]))for (i in1:ncol(data_question1[7:40])) {for (j in1:ncol(data_question1[7:40])) { test_result <-cor.test(data_question1[7:40][, i], data_question1[7:40][, j]) p_matrix2[i, j] <- test_result$p.value }}corr_matrix[which(p_matrix2 >0.05)] <-NA#only keeping significant pval alpha = 0.05melted_corr_matrix_GVar <-melt(corr_matrix[19:34,1:18])ggplot(melted_corr_matrix_GVar, aes(Var1, Var2, fill = value)) +geom_tile() +geom_text(aes(label =ifelse(!is.na(value) &abs(value) >0.75, sprintf("%.2f", value), '')),color ="black", size =2) +scale_fill_gradient2(low ="blue", high ="red", mid ="white",midpoint =0, limit =c(-1, 1), space ="Lab",name ="Spearman\nCorrelation") +theme_minimal() +theme(axis.text.x =element_text(angle =45, hjust =1),axis.text.y =element_text(angle =45, hjust =1)) +labs(x ='Goals', y ='Goals',title ='Correlations Heatmap between goals and our other variables')
>>>>>>> Stashed changes
The numbers are representing the significant pval between our variables. The grey parts are the non significant pvals.
GDP per capita, internet_usage, pf_law or ef_legal are strongely correlated with most of our SDG goals. This is mostly due to the large scope englobed in these variables. That makes them influence various sectors of our economies and thus, mostly impacting all our SDG goals. Therefore, we can think that these variables have a strong impact on the scores. Nevertheless, as correlation doesn’t mean causality, we cannot jump to conclusions.
As we can see, our SDG goals 12 & 13 (responsible consumption & production and climate action) are negatively correlated with most of our variables, as is the economic freedom government variable to our SDG goals. Nevertheless, goals 12 & 13 and ef_government are positively correlated together.
Now let’s zoom on the correlations between all our variables except the SDG goals:
<<<<<<< Updated upstream
Code
melted_corr_matrix_Var <-melt(corr_matrix[19:34,19:34])ggplot(melted_corr_matrix_Var, aes(Var1, Var2, fill = value)) +geom_tile() +geom_text(aes(label =ifelse(!is.na(value) &abs(value) >0.75, sprintf("%.2f", value), '')),color ="black", size =1.7) +scale_fill_gradient2(low ="blue", high ="red", mid ="white",midpoint =0, limit =c(-1, 1), space ="Lab",name ="Spearman\nCorrelation") +theme_minimal() +theme(axis.text.x =element_text(angle =45, hjust =1),axis.text.y =element_text(angle =45, hjust =1)) +labs(x ='Goals', y ='Goals',title ='Correlations Heatmap between other variables than SDG goals')
=======
Code
melted_corr_matrix_Var <-melt(corr_matrix[19:34,19:34])ggplot(melted_corr_matrix_Var, aes(Var1, Var2, fill = value)) +geom_tile() +geom_text(aes(label =ifelse(!is.na(value) &abs(value) >0.75, sprintf("%.2f", value), '')),color ="black", size =1.7) +scale_fill_gradient2(low ="blue", high ="red", mid ="white",midpoint =0, limit =c(-1, 1), space ="Lab",name ="Spearman\nCorrelation") +theme_minimal() +theme(axis.text.x =element_text(angle =45, hjust =1),axis.text.y =element_text(angle =45, hjust =1)) +labs(x ='Goals', y ='Goals',title ='Correlations Heatmap between other variables than SDG goals')
>>>>>>> Stashed changes
As noticed earlier, there is a strong correlation among personal freedom variables (pf), reflecting scores from the Human Freedom Index on movement, religion, assembly, and expression.
Again, we can see that GDP per capita, pf_law, ef_legal are highly correlated with some other variables. On another hand, we notice that pf_movement, pf_assembly, pf_expression are now also higly correlated with some of the other variables.
In order to have a look at the influence of some factors over our dependent variables, let’s conduct a Principal Component Analysis over the Human Freedom Index Scores.
Now concerning the Human Freedom Index scores, most of the variables are positively correlated to the dimension 1, slightly less for the PF religion, and finally the EF government variable is slighlty incorrelated to the dimension 1. With a eigenvalue bigger than 1 for the three first components, we conclude that there are 3 dimensions to take into account. Nevertheless, again, they are explaining less than 80% of cumulated variance. Therefore, the rule of thumb would suggest us to take 4 dimensions into account.
Let’s try now to conduct a cluster analysis, using the Kmean method.
<<<<<<< Updated upstream
Code
data_kmean_country <- data_question1 %>% dplyr::select(-c(X,code,year,continent,region, population))#filter data different than 0 and dropping observations filtered_data <- data_kmean_country %>%group_by(country) %>%filter_if(is.numeric, all_vars(sd(.) !=0)) %>%ungroup()scale_by_country <- filtered_data %>%#scale datagroup_by(country) %>%summarise_all(~scale(.))means_by_country <- scale_by_country %>%#mean by countrygroup_by(country) %>%summarise_all(~mean(., na.rm =TRUE))rownames(means_by_country) <-seq_along(row.names(means_by_country))# Your existing elbow plotelbow_plot <-fviz_nbclust(means_by_country[,-1], kmeans, method="wss", linecolor ="steelblue")# Add a vertical line at the elbow point (4 clusters)elbow_plot_with_line <- elbow_plot +geom_vline(xintercept=4, linetype="dashed", color="red")print(elbow_plot_with_line)
=======
Code
data_kmean_country <- data_question1 %>% dplyr::select(-c(X,code,year,continent,region, population))#filter data different than 0 and dropping observations filtered_data <- data_kmean_country %>%group_by(country) %>%filter_if(is.numeric, all_vars(sd(.) !=0)) %>%ungroup()scale_by_country <- filtered_data %>%#scale datagroup_by(country) %>%summarise_all(~scale(.))means_by_country <- scale_by_country %>%#mean by countrygroup_by(country) %>%summarise_all(~mean(., na.rm =TRUE))rownames(means_by_country) <-seq_along(row.names(means_by_country))# Your existing elbow plotelbow_plot <-fviz_nbclust(means_by_country[,-1], kmeans, method="wss", linecolor ="steelblue")# Add a vertical line at the elbow point (4 clusters)elbow_plot_with_line <- elbow_plot +geom_vline(xintercept=4, linetype="dashed", color="red")print(elbow_plot_with_line)
>>>>>>> Stashed changes
After adapting the data for conducting our cluster analysis, we can see that according the the elbow method that we would only need 4 clusters in our analysis.
Our cluster analysis gives us one principal cluster (here in purple) –> CENTERED ON 0 BECAUSE AFTER DATA SCALED-> REALLY SMALL VALUES –> HOW TO DEAL WITH IT? I TRIED TO TAKE ONLY HFI INTO ACCOUNT BUT NOT WORKING NEITHER. STILL CENTERED ON 0.
##Regressions
While considering our regressions, we have noticed that we had high multicolinearity between our dependent variables in our models. This is due to the numerous variables that we tried to take into account while computing our regressions. Let’s find a model that could explain the overall SDG score without having severe multicollinearity (VIF > 5)
The model found is taking into account the following dependent variables: unemployment rate, military expenditure percentage of GDP, internet_usage, pf_security, pf_religion, pf_identity, ef_legal, ef_trade. We notice here that the previous variables highly correlated to the SDG goals (GDP per capita, pf_law, internet_usage and ef_legal), we dropped the first two ones.
As we can see, all of the variables above are significantly impacting the overall score of our Sustainable Development Goals. In addition, our Radjusted is high enough, which means that our model is well explained.
Code
##### geom point #####geom1 <-ggplot(data_question1, aes(internet_usage, overallscore)) +geom_point()+geom_smooth(se =FALSE) +labs(title ="Scarplot overallscore and internet usage")geom2 <-ggplot(data_question1, aes(unemployment.rate, overallscore)) +geom_point()+geom_smooth(se =FALSE) +labs(title ="Scarplot overallscore and unemployment rate")geom3 <-ggplot(data_question1, aes(MilitaryExpenditurePercentGDP,overallscore)) +geom_point()+geom_smooth(se =FALSE) +labs(title ="Scarplot overallscore and military expenditure")geom4 <-ggplot(data_question1, aes(pf_security,overallscore)) +geom_point()+geom_smooth(se =FALSE) +labs(title ="Scarplot overallscore and pf_security")geom5 <-ggplot(data_question1, aes(pf_religion, overallscore)) +geom_point()+geom_smooth(se =FALSE) +labs(title ="Scarplot overallscore and pf_religion")geom7 <-ggplot(data_question1, aes(pf_identity, overallscore)) +geom_point()+geom_smooth(se =FALSE) +labs(title ="Scarplot overallscore and pf_identity")geom8 <-ggplot(data_question1, aes(ef_legal, overallscore)) +geom_point()+geom_smooth(se =FALSE) +labs(title ="Scarplot overallscore and ef_legal")geom9 <-ggplot(data_question1, aes(ef_trade, overallscore)) +geom_point()+geom_smooth(se =FALSE) +labs(title ="Scarplot overallscore and ef_trade")grid.arrange(geom1, geom2, geom3, geom4, geom5, geom7, geom8, geom9, nrow=3, ncol=3)
As we can see, all of the variables above are significantly impacting the overall score of our Sustainable Development Goals. In addition, our Radjusted is high enough, which means that our model is well explained.
Code
##### geom point #####geom1 <-ggplot(data_question1, aes(internet_usage, overallscore)) +geom_point()+geom_smooth(se =FALSE) +labs(title ="Scarplot overallscore and internet usage")geom2 <-ggplot(data_question1, aes(unemployment.rate, overallscore)) +geom_point()+geom_smooth(se =FALSE) +labs(title ="Scarplot overallscore and unemployment rate")geom3 <-ggplot(data_question1, aes(MilitaryExpenditurePercentGDP,overallscore)) +geom_point()+geom_smooth(se =FALSE) +labs(title ="Scarplot overallscore and military expenditure")geom4 <-ggplot(data_question1, aes(pf_security,overallscore)) +geom_point()+geom_smooth(se =FALSE) +labs(title ="Scarplot overallscore and pf_security")geom5 <-ggplot(data_question1, aes(pf_religion, overallscore)) +geom_point()+geom_smooth(se =FALSE) +labs(title ="Scarplot overallscore and pf_religion")geom7 <-ggplot(data_question1, aes(pf_identity, overallscore)) +geom_point()+geom_smooth(se =FALSE) +labs(title ="Scarplot overallscore and pf_identity")geom8 <-ggplot(data_question1, aes(ef_legal, overallscore)) +geom_point()+geom_smooth(se =FALSE) +labs(title ="Scarplot overallscore and ef_legal")geom9 <-ggplot(data_question1, aes(ef_trade, overallscore)) +geom_point()+geom_smooth(se =FALSE) +labs(title ="Scarplot overallscore and ef_trade")grid.arrange(geom1, geom2, geom3, geom4, geom5, geom7, geom8, geom9, nrow=3, ncol=3)
>>>>>>> Stashed changes
By checking the influence of the chosen variables over the overallscore, we can see that the functions are not linear. For some, such as internet_usage and ef_legal, we notice that the more the variable increase, the more it influence positively the overall score. For the others, the relations are more complex. I.e.: Unemployment.rate increase mostly the overallscore between 0 and 10%. pf_identity is slowly reducing the overallscore before going back up.
In conclusion, after reviewing which variables are correlating between themself, after taking care of multicollinearity problems and doing our regressions on our overall SDG score and finally seeing the influence of these dependent variables depending on their range, we notice that most of our variables taken into account in our model is significant in explaining their influence (positive or negative) over the overall SDG goals. As our goals are mostly correlated between eachother, we can presume that taking the overall score as our dependent variable is giving us the same conclusion. Nevertheless, we still need to go deeper and check the influence of the scores between themself.
